T1 shortening

T1 shortening DEFAULT

Contrast FLAIR tops FLAIR and contrast T1-weighted images

When considering which is the most sensitive MRI technique for detecting disease in the brain, the choice is usually between contrast-enhanced T1-weighted imaging and T2-weighted fluid-attenuated inversion recovery (FLAIR) imaging. Twelve years ago, however, we found that contrast-enhanced FLAIR was actually more sensitive than either,1 particularly for subtle cortical lesions (Figure 1). This sensitivity is likely due to the combination of T2 prolongation, the usual mechanism for parenchymal hyperintensity on FLAIR images, and T1 shortening from the gadolinium chelate. While we normally think of T2 prolongation as the only mechanism for increasing signal intensity on T2-weighted FLAIR images, T1 shortening can also increase signal if it hasn’t maxed out yet. Since T2 prolongation and T1 shortening are synergistic, contrast-enhanced FLAIR is more sensitive for subtle abnormalities than either FLAIR alone or postcontrast T1-weighted imaging alone.

The greatest advantage of contrast-enhanced FLAIR seems to be for detecting subtle cortical abnormalities such as leptomeningeal carcinomatosis (Figure 1), where there is no mass effect. However, it also has an advantage when compared with conventional FLAIR or T2-weighted imaging for deep lesions (Figure 2), where the contrast between the enhancing lesion and the surrounding vasogenic edema is greater than with conventional techniques, and produces very remarkable images.

A reason contrast-enhanced FLAIR is especially sensitive for subtle cortical abnormalities may be that a small amount of gadolinium leaks into the adjacent sulcus. While this may not produce enough T1 shortening to be seen on a T1-weighted image, it may shorten the T1 of cerebrospinal fluid so that it is no longer nulled by the initial 180o pulse in the inversion recovery FLAIR technique.

Contrast-enhanced FLAIR need not take longer than a conventional contrast-enhanced brain study. Since unenhanced FLAIR is usually a part of an MR brain exam, we merely acquire it after contrast is given, rather than before, as is usually done. In the beginning we would acquire T1-weighted images with and without contrast as well as FLAIRs with and without contrast. However, with time, we found we did not need the precontrast FLAIR. Comparing T1-weighted images pre- and postcontrast demonstrated enhancement, and any additional signal on the contrast-enhanced FLAIR we found to be due to T2 prolongation. Since most enhancing lesions generally also produce vasogenic edema, which prolongs T2, the two contrast mechanisms are synergistic.

When reading out brain studies performed with contrast enhancement, I usually read the contrast-enhanced FLAIRs first. If they are normal, it is unlikely that the enhanced T1-weighted images or the unenhanced FLAIRs will add anything. If they are abnormal, the pre- and post–T1-weighted images can sort out whether the brightness on the contrast-enhanced FLAIRs is due more to contrast enhancement or T2 prolongation.

Sours: https://www.diagnosticimaging.com/view/contrast-flair-tops-flair-and-contrast-t1-weighted-images

Cubic versus hexagonal – effect of host crystallinity on the T1 shortening behaviour of NaGdF4 nanoparticles†

Author affiliations

* Corresponding authors

a Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie St. Ottawa (ON) K1N 6N5, Canada
E-mail:[email protected]

b Department of Medical Imaging, The Ottawa Hospital, 501 Smyth Rd. Ottawa (ON) K1H 8L6, Canada

c Department of Radiology, University of Ottawa, 501 Smyth Rd. Ottawa (ON) K1H 8L6, Canada

d Ottawa Hospital Research Institute, 501 Smyth Rd. Ottawa (ON) K1H 8L6, Canada

e University of Ottawa Heart Institute, 40 Ruskin St. Ottawa (ON) K1Y 4W7, Canada

f Centre for Advanced Materials Research (CAMaR), University of Ottawa, Ottawa (ON) K1N 6N5, Canada


Sodium gadolinium fluoride (NaGdF4) nanoparticles are promising candidates as T1 shortening magnetic resonance imaging (MRI) contrast agents due to the paramagnetic properties of the Gd3+ ion. Effects of size and surface modification of these nanoparticles on proton relaxation times have been widely studied. However, to date, there has been no report on how T1 relaxivity (r1) is affected by the different polymorphs in which NaGdF4 crystallizes: cubic (α) and hexagonal (β). Here, a microwave-assisted thermal decomposition method was developed that grants selective access to NaGdF4 nanoparticles of either phase in the same size range, allowing the influence of host crystallinity on r1 to be investigated. It was found that at 3 T cubic NaGdF4 nanoparticles exhibit larger r1 values than their hexagonal analogues. This result was interpreted based on Solomon–Bloembergen–Morgan theory, suggesting that the inner sphere contribution to r1 is more pronounced for cubic NaGdF4 nanoparticles as compared to their hexagonal counterparts. This holds true irrespective of the chosen surface modification, i.e. small citrate groups or longer chain poly(acrylic acid). Key aspects were found to be a polymorph-induced larger hydrodynamic diameter and the higher magnetization possessed by cubic nanoparticles.

Graphical abstract: Cubic versus hexagonal – effect of host crystallinity on the T1 shortening behaviour of NaGdF4 nanoparticles

You have access to this article

Please wait while we load your content... Something went wrong. Try again?

Supplementary files

Article information


Article type

08 Jan 2019

17 Mar 2019

First published
18 Mar 2019

Nanoscale, 2019,11, 6794-6801

Cubic versus hexagonal – effect of host crystallinity on the T1 shortening behaviour of NaGdF4 nanoparticles

N. Liu, R. Marin, Y. Mazouzi, G. O. Cron, A. Shuhendler and E. Hemmer, Nanoscale, 2019, 11, 6794 DOI: 10.1039/C9NR00241C

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Fetching data from CrossRef.
This may take some time to load.

Loading related content



Sours: https://pubs.rsc.org/en/content/articlelanding/2019/nr/c9nr00241c
  1. Valley pivot tires
  2. Queer eye memes
  3. Jbl headphone driver

An MRI sequence is a number of radiofrequency pulses and gradients that result in a set of images with a particular appearance. This article presents a simplified approach to recognizing common MRI sequences, but does not concern itself with the particulars of each sequence.

For a more complete and accurate discussion please refer to MRI pulse sequences.

On this page:

The simplest way to think about the multitude of sequences available on modern scanners is to divide them according to the dominant influence on the appearance of tissues. This leads to a division of all sequences into proton density (PD) weighted, T1 weighted, T2 weighted, diffusion weighted, flow sensitive and 'miscellaneous'. A number of 'optional add-ons' can also be considered, such as fat or fluid attenuation, or contrast enhancement. This leads to a broad categorization as follows:

  • T1
    • gadolinium enhanced
    • fat suppressed
  • T2
    • fat suppressed
    • fluid attenuated
    • susceptibility sensitive
  • proton density
  • diffusion weighted
  • flow sensitive
    • MR angiography
    • MR venography
    • CSF flow studies
  • miscellaneous
    • MR cholangiopancreatography (MRCP)
      • a special T2-weighted sequence
    • MR spectroscopy
    • MR perfusion
    • functional MRI
    • tractography

ADVERTISEMENT: Supporters see fewer/no ads


When describing most MRI sequences we refer to the shade of grey of tissues or fluid with the word intensity, leading to the following absolute terms:

  • high signal intensity = white
  • intermediate signal intensity = grey
  • low signal intensity = black

Often we refer to the appearance by relative terms:

  • hyperintense = brighter than the thing we are comparing it to
  • isointense = same brightness as the thing we are comparing it to
  • hypointense = darker than the thing we are comparing it to

Annoyingly these relative terms are used without reference to the tissue being used as the comparison. In some instances this does not lead to any problems; for example, a hyperintense lesion in the middle of the liver is clearly hyperintense compared to the surrounding liver parenchyma. In many other situations however use of relative terms leads to potential confusion. Imagine a lesion within the ventricles of the brain described as "hypointense". Does this denote a lesion darker than CSF or than the adjacent brain?

As such it is preferable to either use absolute terminology or, if using relative terms, to acknowledge the comparison tissue e.g. "the lesion is hyperintense to the adjacent spleen".

NB: the word density is for CT, and there are few better ways to show yourself as an MRI noob than by making this mistake.


When describing diffusion weighted sequences, we also use the term intensity but additionally we use the words restricteddiffusion and facilitateddiffusion to denote whether water can move around less easily (restricted) or more easily (facilitated) than expected for that tissue. Again many use these words as if they are absolute terms and this leads to confusion (more on this issue here).

ADVERTISEMENT: Supporters see fewer/no ads

T1 weighted sequences are part of almost all MRI protocols and are best thought of as the most 'anatomical' of images, resulting in images that most closely approximate the appearances of tissues macroscopically, although even this is a gross simplification.

The dominant signal intensities of different tissues are:

  • fluid (e.g. urine, CSF): low signal intensity (black)
  • muscle: intermediate signal intensity (grey)
  • fat: high signal intensity (white)
  • brain
    • grey matter: intermediate signal intensity (grey)
    • white matter: hyperintense compared to grey matter (white-ish)

Read more about T1 weighted sequences.

Contrast enhanced

The most commonly used contrast agents in MRI are gadolinium based. At the concentrations used, these agents have the effect of causing T1 signal to be increased (this is sometimes confusingly referred to as T1 shortening). The contrast is injected intravenously (typically 5-15 mL) and scans are obtained a few minutes after administration. Pathological tissues (tumors, areas of inflammation / infection) will demonstrate accumulation of contrast (mostly due to leaky blood vessels) and therefore appear as brighter than surrounding tissue. Often post contrast T1 sequences are also fat suppressed (see below) to make this easier to appreciate.

Fat suppression

Fat suppression (or attenuation or saturation) is a tweak performed on many T1 weighted sequences, to suppress the bright signal from fat. This is performed most commonly in two scenarios:

Firstly, and most commonly, after the administration of gadolinium contrast. This has the advantage of making enhancing tissue easier to appreciate.

Secondly, if you think that some particular tissue is fatty and want to prove it, showing that it becomes dark on fat suppressed sequences is handy.

Read more about fat suppressed sequences.

ADVERTISEMENT: Supporters see fewer/no ads

T2 weighted sequences are part of almost all MRI protocols. Without modification the dominant signal intensities of different tissues are:

  • fluid (e.g. urine, CSF): high signal intensity (white)
  • muscle: intermediate signal intensity (grey)
  • fat: high signal intensity (white)
  • brain
    • grey matter: intermediate signal intensity (grey)
    • white matter: hypointense compared to grey matter (dark-ish)

Read more about T2 weighted sequences.

Fat suppressed

In many instances one wants to detect edema in soft tissues which often have significant components of fat. As such suppressing the signal from fat allows fluid, which is of high signal, to stand out. This can be achieved in a number of ways (e.g. chemical fat saturation or STIR) but the end result is the same.

Read more about fat suppressed sequences.

Fluid attenuated

Similarly in the brain, we often want to detect parenchymal edema without the glaring high signal from CSF. To do this we suppress CSF. This sequence is called FLAIR. Importantly, at first glance FLAIR images appear similar to T1 (CSF is dark). The best way to tell the two apart is to look at the grey-white matter. T1 sequences will have grey matter being darker than white matter. T2 weighted sequences, whether fluid attenuated or not, will have white matter being darker than grey matter.

Read more about FLAIR sequence.

Susceptibility sensitive sequences

Being able to detect blood products or calcium is important in many pathological processes. MRI offers a number of techniques that are sensitive to these sort of compounds. Generally these sequences exploit what is referred to as T2* (T2 star) which is highly sensitive to small perturbations in the local magnetic field. The most sensitive of these sequences is known as susceptibility weighted imaging (SWI) and is also able to distinguish calcium from blood.

Read more about susceptibility weighted imaging (SWI).

ADVERTISEMENT: Supporters see fewer/no ads

Given that nuclear magnetic resonance of protons (hydrogen ions) forms the major basis of MRI, it is not surprising that signal can be weighted to reflect the actual density of protons; an intermediate sequence sharing some features of both T1 and T2.

Proton density images were extensively used for brain imaging, however they have largely been replaced by FLAIR. PD however continues to offer excellent signal distinction between fluid, hyaline cartilage and fibrocartilage, which makes this sequence ideal in the assessment of joints. 

The dominant signal intensities of different tissues are:

  • fluid (e.g. joint fluid, CSF): high signal intensity (white)
  • muscle: intermediate signal intensity (grey)
  • fat: high signal intensity (white)
  • hyaline cartilage: intermediate signal intensity (grey)
  • fibrocartilage: low signal intensity (black)

Diffusion weighted imaging assess the ease with which water molecules move around within a tissue (mostly representing fluid within the extracellular space) and gives insight into cellularity (e.g. tumors), cell swelling (e.g. ischemia) and edema.

The dominant signal intensities of different tissues are:

  • fluid (e.g. urine, CSF): no restriction to diffusion
  • soft tissues (muscle, solid organs, brain): intermediate diffusion
  • fat: little signal due to paucity of water

Typically you will find three sets of images when diffusion weighted imaging is performed: DWI, ADC and B=0 images.


When we say "DWI" we usually are referring to what is in better terms an isotropic T2 weighted map as it represents the combination of actual diffusion values and T2 signal.

It is a relatively low resolution image with the following appearance:

  • grey matter: intermediate signal intensity (grey)
  • white matter: slightly hypointense compared to grey matter
  • CSF: low signal (black)
  • fat: little signal due to paucity of water
  • other soft tissues: intermediate signal intensity (grey)

Acute pathology (ischemic stroke, cellular tumor, pus) usually appears as increased signal denoting restricted diffusion. However (and importantly), because there is a component of the image derived from T2 signal, some tissues that are bright on T2 will appear bright on DWI images without there being an abnormal restricted diffusion. This phenomenon is known as T2 shine through.


Apparent diffusion coefficient maps (ADC) are images representing the actual diffusion values of the tissue without T2 effects. They are therefore much more useful, and objective measures of diffusion values can be obtained, however they are much less pretty to look at. They appear basically as grayscale inverted DWI images.

They are relatively low resolution images with the following appearances:

  • grey matter: intermediate signal intensity (grey)
  • white matter: slightly hyperintense compared to grey matter
  • CSF: high signal (white)
  • fat: little signal due to paucity of water
  • other soft tissues: intermediate signal intensity (grey)

Acute pathology (ischemic stroke, cellular tumor, pus) usually appears as decreased signal denoting restricted diffusion.


If you see these, do not worry. They are only used to calculate ADC values. They are essentially T2 weighted images with a bit of susceptibility effects.

Read more about diffusion weighted imaging.

ADVERTISEMENT: Supporters see fewer/no ads

One of the great advantages of MRI is its ability to image physiological flow (e.g. blood flow) often without the need for intravenous contrast. This allows for the imaging of arteries, veins and CSF flow.

Read more about MR angiography.

Read more about MR venography.

Read more about CSF flow studies.

In addition to the aforementioned sequences, novel applications have been developed over the years, largely beyond the scope of this introductory article.

MR spectroscopy

Different compounds interact with the magnetic field of MRI scanners slightly differently and the amounts of these compounds can be detected in a quantifiable way in a prescribed region of tissue. These can be used to help characterize the tissue to aid in diagnosis or grading of tumors.

Read more about MR spectroscopy.

MR perfusion

The amount of blood flowing into tissue can also be detected and relatively quantified, generating values such as cerebral blood volume, cerebral blood flow and mean transit time. These values are useful in a number of clinical scenarios, including defining the ischemic penumbra in ischemic stroke, assessing histological grade of certain tumors, or distinguishing radionecrosis from tumor progression.

Read more about MR perfusion.

Functional MRI

The brain controls its blood flow very tightly and locally. Active tissue demonstrates elevated blood flow and this can be detected.

Read more about functional MRI.


The structure of tissue (e.g. axons tightly packed together) influences how easily diffusion of water occurs in various directions. This can be detected and the direction of white matter tracts can be implied.

Read more about tractography.

Quiz questions


Related articles: Imaging technology

Promoted articles (advertising)

Sours: https://radiopaedia.org/articles/mri-sequences-overview?lang=us
#214- Ochrona T1/ Regulacja/Odżywanie w pszenicy ozimej! Najważniejszy zabieg! Sucho w polu!

Shortened Modified Look-Locker Inversion recovery (ShMOLLI) for clinical myocardial T1-mapping at 1.5 and 3 T within a 9 heartbeat breathhold

Journal of Cardiovascular Magnetic Resonancevolume 12, Article number: 69 (2010) Cite this article

  • 36k Accesses

  • 424 Citations

  • 6 Altmetric

  • Metrics details



T1 mapping allows direct in-vivo quantitation of microscopic changes in the myocardium, providing new diagnostic insights into cardiac disease. Existing methods require long breath holds that are demanding for many cardiac patients. In this work we propose and validate a novel, clinically applicable, pulse sequence for myocardial T1-mapping that is compatible with typical limits for end-expiration breath-holding in patients.

Materials and methods

The Shortened MOdified Look-Locker Inversion recovery (ShMOLLI) method uses sequential inversion recovery measurements within a single short breath-hold. Full recovery of the longitudinal magnetisation between sequential inversion pulses is not achieved, but conditional interpretation of samples for reconstruction of T1-maps is used to yield accurate measurements, and this algorithm is implemented directly on the scanner. We performed computer simulations for 100 ms<T1 < 2.7 s and heart rates 40-100 bpm followed by phantom validation at 1.5T and 3T. In-vivo myocardial T1-mapping using this method and the previous gold-standard (MOLLI) was performed in 10 healthy volunteers at 1.5T and 3T, 4 volunteers with contrast injection at 1.5T, and 4 patients with recent myocardial infarction (MI) at 3T.


We found good agreement between the average ShMOLLI and MOLLI estimates for T1 < 1200 ms. In contrast to the original method, ShMOLLI showed no dependence on heart rates for long T1 values, with estimates characterized by a constant 4% underestimation for T1 = 800-2700 ms. In-vivo, ShMOLLI measurements required 9.0 ± 1.1 s (MOLLI = 17.6 ± 2.9 s). Average healthy myocardial T1 s by ShMOLLI at 1.5T were 966 ± 48 ms (mean ± SD) and 1166 ± 60 ms at 3T. In MI patients, the T1 in unaffected myocardium (1216 ± 42 ms) was similar to controls at 3T. Ischemically injured myocardium showed increased T1 = 1432 ± 33 ms (p < 0.001). The difference between MI and remote myocardium was estimated 15% larger by ShMOLLI than MOLLI (p < 0.04) which suffers from heart rate dependencies for long T1. The in-vivo variability within ShMOLLI T1-maps was only 14% (1.5T) or 18% (3T) higher than the MOLLI maps, but the MOLLI acquisitions were twice longer than ShMOLLI acquisitions.


ShMOLLI is an efficient method that generates immediate, high-resolution myocardial T1-maps in a short breath-hold with high precision. This technique provides a valuable clinically applicable tool for myocardial tissue characterisation.


In cardiovascular magnetic resonance (CMR), tissue contrast is generated by a combination of intrinsic tissue properties such as spin-lattice (T1) and spin-spin (T2) relaxation times, and extrinsic properties such as imaging sequence and settings. Signal intensity in conventional CMR images is displayed on an arbitrary scale, and thus is not suited to comparison between subjects. T1-mapping provides a quantitative surrogate marker for the cellular environment of the myocardial water allowing direct comparison between patients and examinations, which can operate without the need for exogenous contrast agents.

T1 relaxation times depend on the composition of tissues, and each tissue type exhibits a characteristic range of normal values at a selected magnetic field strength [1]. Deviation from established ranges can thus be used to quantify the effects of pathological processes. Focal and global T1 changes are reported in a number of myocardial diseases, such as myocardial infarction [2, 3], heart failure [4], valvular heart disease [5], and systemic diseases with cardiac involvement such as amyloidosis [6, 7] or systemic lupus erythematosus [8]. T1-mapping may be a sensitive technique for detecting diffuse fibrosis in heart failure and valvular heart disease, which have been described by abnormal post-contrast T1 values but not by conventional late gadolinium enhancement (LGE) imaging [4, 5].

An established method for myocardial T1-mapping is the modified Look Locker inversion recovery (MOLLI) pulse sequence [9]. It merges images from three consecutive inversion-recovery (IR) experiments into one data set (Figure 1A), generating single-slice T1 maps of the myocardium [9, 10]. Clinical use of MOLLI was demonstrated in myocardial infarction [2, 3]. One barrier to the clinical adoption of this method is the 17 heart beat breath-hold required to obtain a single slice T1 map. This is usually acceptable for normal subjects who, on average, can hold their breath for 20.9 s (range 13-74 s) in end-expiration [11] (the respiratory phase in which CMR is usually performed). However, patients with pulmonary compromise could only achieve such breath holds for 9.1 s (range 2-16 s) [11]. Long breath holds are problematic in the elderly population due to frequent respiratory comorbidities [12] and in patients on beta-blockers where lower heart rates translate into breath holds of over 20 seconds. Therefore, a significant improvement in speed over the MOLLI method is required to achieve T1 mapping for sequential slice by slice whole heart coverage in routine clinical practice.

ECG-gated pulse sequence schemes for simulation of A) MOLLI and B) ShMOLLI at a heart rate of 60 bpm. SSFP readouts are simplified to a single 35° pulse each, presented at a constant delay time TD from each preceding R wave. The 180° inversion pulses are shifted depending on the IR number to achieve the desired first TI of 100, 180 and 260 ms in the consecutive inversion recovery (IR) experiments. The plots below represent the evolution of longitudinal magnetisation (Mz) for short T1 (400 ms, thin lines) and long T1 (2000 ms, thick lines). Note that long epochs free of signal acquisitions minimise the impact of incomplete Mz recoveries in MOLLI so that all acquired samples can be pooled together for T1 reconstruction. In ShMOLLI the validity of additional signal samples from the 2nd and 3rd IR epochs is determined by progressive nonlinear estimation.

Full size image

We hypothesise that it is possible to determine T1 maps of the heart with high precision in a short breath-hold. We thus present a shortened alternative to MOLLI (ShMOLLI) which can generate rapid and high-resolution myocardial T1-maps in a single short breath-hold of only 9 heartbeats. We investigate the accuracy of T1 measurements by ShMOLLI against MOLLI in simulation and gel phantom experiments over a wide range of heart rates and T1 values. The findings are validated in-vivo for normal human myocardium at 1.5T and 3T. To extend the in-vivo validation range we also present findings in four human subjects with recent myocardial infarction and four normal subjects after gadolinium contrast.

Materials and methods

Simulation and sequence design

Simulations were performed in IDL (Interactive Data Language ver. 6.1, ITT Visual Information Solutions) by implementing equations for the piece-wise calculation of longitudinal magnetisation (Mz(t)) and the signal samples generated by a train of arbitrarily-spaced ideal excitation pulses. Inversion pulses are assumed to be perfect 180° excitations; readouts are simulated as single pulses of 35°. Both sequences had three IR epochs. Simulations were performed for MOLLI based on its optimised variant [13], which collects 3+3+5 samples in three consecutive IR epochs separated by long recovery periods, which are approximated as being fully recovered in the reconstruction. The shortest effective TI [9] in each IR are 100, 180 and 260 ms; incremented by heart beat period for subsequent samples (Figure 1A). ShMOLLI uses a similar effective TI principle but collects only 5+1+1 samples and IR epochs are separated by only one TRR (R-R interval), which does not approximate to full recovery of the magnetization (Figure 1B).

Simulations using simplified pulse sequences outlined in Figure 1 were performed for T1 ranging from 50 to 2700 ms (50 ms increments) and for HR between 40-100 bpm (20 bpm increments). For each combination of parameters, we analysed 200 sample trains with noise representative of our phantom measurements.


Significant shortening of the recovery epochs means that Mz can be severely affected by preceding IR epochs in the excitation sequence as demonstrated for long T1 values in Figure 2B (thick grey line). Consequently, the signal samples from the 2nd and 3rd IR of ShMOLLI do not fit the simple method of pooling data together into a single IR equation as outlined for MOLLI [9] and would produce significant errors if used in reconstruction. This problem is circumvented by conditional data analysis according to the algorithm presented in Figure 2. In essence, in regions of long T1, samples 1-5 are fitted using [9]. For shorter T1 (0.4TRR < T1 < TRR) samples 1-6 are used. For very short T1 (< 0.4TRR), samples 1-7 are utilised. As the T1 is not known accurately the inclusion thresholds 0.4TRR and TRR are modified depending on the fit error.

ShMOLLI conditional data analysis. Simplified algorithm for inclusion of samples to circumvent the impact of short recovery epochs in T1 estimation. "FE" is the fit error calculated as the square root of the sum of squared residuals divided by number of samples minus one. "S1-5" denotes the set of samples from the first inversion recovery, "S1-6" and" S1-7" are supplemented by samples from consecutive IR experiments. TR-R is a heart beat interval.

Full size image

On-the-scanner implementation

The MOLLI and ShMOLLI acquisitions used a research sequence provided by Siemens (Siemens Healthcare, Germany) utilising the optimised parameter set for MOLLI [13] and ShMOLLI. T1 s were fitted using the algorithms described above with an AMOEBA optimisation method [14]. This reconstruction fitting was coded in C++ directly into the scanner's multi-threaded parallel processing image pipeline. As a result, T1-maps are available for viewing in about 10 seconds following image acquisition.

Phantom studies

Fifteen 50 ml Agarose and NiCl gel phantoms [15] with T2~50 ms and T1 ~100-2500 ms were studied at 1.5T (Avanto, Siemens Healthcare, Germany) using a 32 channel body array and at 3T (Trio, Siemens Healthcare) using a 16 channel body array. The artificially generated ECG scanner triggers were used to validate HR range from 40-100 bpm with 10 bpm increments. Imaging parameters were the same for MOLLI and ShMOLLI: FOV = 300 × 300 mm, voxel size = 1.17 × 1.17 × 8 mm, matrix = 256 × 256 (interpolated from 128 × 128 acquisition matrix, 95 phase encoding steps), flip angle = 35°, TR/TE = 1.96/0.98 ms.

Reference T1 relaxation times were calculated offline based on images collected using slice-selective IR with non-segmented spin echo readout. TI = 33, 100, 300, 900, 2700, 5000 ms. TR/TE = 10 s/6.3 ms; 80 phase encoding steps with total image acquisition time of 13 minutes. Sizing and positioning were identical to the studied MOLLI methods. Regions of interest were placed in each tube using an automated method and reference T1 s were fitted per pixel and a mean T1 determined.


Ethics approval was granted for all study procedures and informed consent was obtained from all subjects.

Human volunteers

10 normal volunteers (7 men; age 35 ± 7 years, normal ECGs without history of cardiac diseases or symptoms) underwent CMR imaging at 1.5T and 3T on the same day. Following standard planning, end-expiration basal, mid-cavity and apical short-axis images using MOLLI and ShMOLLI were collected. Images for specific TI were collected using exactly the same SSFP readouts for both methods to allow direct comparisons, typically: TR/TE = 2.14/1.07 ms, flip angle = 35°, FOV = 340 × 255 mm, matrix = 192 × 144, 107 phase encoding steps, interpolated voxel size = 0.9 × 0.9 × 8 mm, GRAPPA = 2 with 24 reference lines, cardiac delay time TD = 500 ms; 206 ms acquisition time for single image. A single slice, which was judged to have the "best quality" at the time of scanning, was repeated twice at the end of the protocol to assess short-term intra-scan variability of the T1 measurements; this was not performed in the first pilot case. Offline post-processing involved manual tracing of endo- and epi-cardial contours for analysis of the T1 measurements in myocardial segments 1 to 16 of the American Heart Association (AHA) 17-segment model [16] using in-house software.

Gadolinium contrast application

Matching pairs of ShMOLLI and MOLLI pre-contrast and post-contrast T1 maps were obtained in 4 female subjects (61 ± 3 years old) without pre-existing cardiac disease who underwent a separate research protocol at 1.5T. Subjects underwent adenosine stress perfusion at 140 μg/kg/min for 3 min, followed by a bolus of Gd (Gadodiamide, Omniscan, GE Healthcare, Amersham, UK, 0.03 mmol/kg body weight). After 20 minutes, resting perfusion imaging was performed using 0.03 mmol/kg of Gd followed immediately by a top-up Gd of 0.10 mmol/kg for LGE imaging. Matching T1-maps were obtained at baseline and ~14 minutes after adenosine stress perfusion. Finally, 4 pairs of images were collected before, and one after, the LGE images. The dynamic evolution of T1 recovery after the final Gd bolus was corrected with 3rd order polynomial for the purpose of constructing Bland-Altman plots.

Patients with recent myocardial infarction

4 patients (3 men; age 53 ± 10 years) underwent CMR at 3T following the diagnosis of a first acute ST-elevation type myocardial infarction (STEMI) post primary percutaneous coronary intervention (PCI). LGE images were obtained 24-48 hours post acute infarct [17]. T1-maps using ShMOLLI and MOLLI were obtained 5-17 days after the ischemic event at a single representative slice replicating settings from the normal control volunteer study. Manual contouring of the endo- and epicardium was followed by calculation of the distribution of T1 values within the defined myocardium. The resulting distributions were clearly bi-modal and were fitted using a two-component Gaussian model in order to assess T1 in injured and unaffected myocardium.


Unless stated otherwise, the results are presented as mean ± SD/mixed SD, where the "mixed SD" is estimated as the independent combination of average individual SD and the interindividual SD. For estimation of the relative variability we use the coefficient of variation (CV = 100%*SD/Mean). The term "noise penalty" is used to describe the expected relative increase in CV resulting from predicable factors, i.e. the reduced number of samples. Significance of the differences between population means is calculated using 2-tailed Student T-tests, paired whenever possible, and quoted when p <0.05.


Simulation and phantom studies

The accuracy of both methods is dependent on the T1 and heart rate. Generally, as HR and T1 increase, the T1 measurements diverge increasingly from the ideal identity line (Figure 3). This is observed in both simulation (Figure 3A, C) and phantom measurements with excellent agreement at both field strengths (Figure 3B, D). Figure 3C, D demonstrate a clear dependence of MOLLI estimates on HR, with relative underestimation reaching -30% for the highest HR and longest T1 studied. In contrast, ShMOLLI calculation (Figure 3A, B) demonstrates tight overlap of the values with significant reduction in dependence on the heart rate. For T1 s longer than approximately 800 ms the deviation of ShMOLLI estimates from the identity line is proportional to T1. The corresponding relative error of underestimation by ShMOLLI in this range is effectively a constant of -4%.

Relationship between T1 measurements for ShMOLLI (top row) and MOLLI (bottom row) and the corresponding reference T1 (T1ref) depending on selected simulated heart rates (HR). Diagonal line represents the ideal identity line. Simulation (A&C) and Phantom (B&D) measurements in 3T and 1.5T (dashed lines) overlap and are in close agreement with simulation results. NOTE. The lines are offset by 15 ms horizontally for each HR value to reduce the overlap between lines.

Full size image

The variability of T1 measurements across all simulations or pixels is shown as whiskers in Figure 3. For long T1 s, measurements by ShMOLLI are based on 5 samples whereas MOLLI measurements are based on 11 samples. Therefore ShMOLLI has a predicted noise penalty of 48% versus MOLLI (√(11/5)). For shorter T1 s, 6 or 7 TI samples are used with a predicted noise penalty of 35% and 25%. Inclusive of the heavily nonlinear processing, for the reference T1 range of 300-2600 ms, we find the simulated average CV is 2.7% for ShMOLLI and 2.1% for MOLLI, translating to an overall 28% noise penalty for using ShMOLLI. In phantom studies, the noise penalty for ShMOLLI is 21% at 3T (Figure 3B) and 61% at 1.5T (Figure 3C).

Normal human subjects

In normal control volunteers, the myocardial ShMOLLI and MOLLI T1-maps did not show any visual differences (Figure 4). The imaging time was 9.0 ± 1.1 s and 17.6 ± 2.9 s, respectively. Both methods utilise the same imaging SSFP sequence and suffered from occasional localised image artefacts. These were more severe at 3T where the maximum CV over the worst single segment reached 79% for MOLLI and 47% for ShMOLLI. Aside from the extreme outliers, the accuracy of T1 measurements benefited both from the increase in field strength and the number of samples included. CV was below 10% in 97% of the segments for MOLLI and 94% for ShMOLLI at 3T. At 1.5T the proportions were 89% and 85%, respectively. The median CVs were 4.7% (MOLLI, 3T), 5.5% (ShMOLLI, 3T), 6.1% (MOLLI, 1.5T) and 7.4% (ShMOLLI, 1.5T). Overall, ShMOLLI carried a 10% noise penalty at 1.5T (p < 0.001) but was the same as MOLLI at 3T (3%, p = 0.8). The lack of statistical significance in the latter comparison was attributed to outliers, which was addressed by removing 3 (out of 160) data points from further analysis. This returned the expected differences (p < 0.001) between methods indicating an 18% noise penalty for ShMOLLI at 3T and 14% at 1.5T. There was a clear benefit of using high field MR in a 33% reduction in CV for MOLLI and 24% for ShMOLLI (p < 0.001, both differences) despite the use of a better coil array at 1.5T.

Representative short axis slice T1 maps of the normal myocardium obtained using MOLLI (top row) and ShMOLLI (bottom row) at 1.5T at the baseline and following Gd administration for perfusion imaging (1st Gd) and after top-up (2nd Gd) at times shown in panel labels.

Full size image

The average T1 values for myocardial segments derived using both ShMOLLI and MOLLI at 1.5T and 3T are presented in Figure 5. At 1.5T, average myocardial T1 s by ShMOLLI were 966 ± 48/88 ms and 976 ± 46/80 ms by MOLLI. T1 values for normal myocardium by both methods compared closely to previously published T1 values at 1.5T [10]. At 3T, average myocardial T1 s were similar for ShMOLLI and MOLLI (1166 ± 60/91 ms and 1169 ± 45/73 ms).

T1 values for AHA myocardial segments 1-16 (see insert) at 1.5T and 3T using ShMOLLI and MOLLI. Results of current study as compared to previously published values at 1.5T [10].

Full size image

Analysis of differences between methods (Figure 6) revealed that myocardial ShMOLLI T1 s were shorter than MOLLI T1 s by 10 ± 16 ms (relative 1 ± 1.6%, p < 0.001) at 1.5T. At 3T both methods produced similar values (Δ =-0.2 ± 16 ms or 0.02 ± 1.3%, p = 0.13). Given that the average HR was 63 ± 8 bpm (1.5T) and 61 ± 7 bpm (3T), for the closest emulated HR of 60 bpm, we predict from phantoms the difference between methods to be -12.5 ms (for normal myocardial T1 = 1000 ms at 1.5T) and +0.5 ms (for normal myocardial T1 = 1200 ms at 3T). Analysis of intra-scan variability showed that the average repetition error was similar to the spread of differences between methods, approximately ± 16 ms (~1.6%) between any pair of repeated measurements at 1.5T. At 3T the intra-scan variability was ± 29 ms (2.4%) for ShMOLLI and ± 76 ms (7%) for MOLLI. The latter was due to the 3 myocardial segments characterised by excessive CV of over 38%. When these were removed, MOLLI repetition accuracy improved to ± 18 ms (1.5%).

Bland-Altman plot shows good agreement between the average ShMOLLI and MOLLI myocardial T1 values pooled across our material obtained at 1.5T and 3T. Note that the distribution of differences at approximately ± 17 ms is also representative of the repetition accuracy for either method. Note that the majority of outliers relate to the infarct data at 3T where the demonstrated MOLLI bias dominates over noise considerations even at normal heart rates. Thick dashed line represents overall average difference between measurements (-3 ms), thin dashed lines represent 2SD range.

Full size image

Gadolinium contrast application

None of the subjects demonstrated LGE lesions. The baseline T1 values were 982 ± 28 ms, comparable with other controls at 1.5T. Depending on the dose of gadolinium and time point the T1 showed expected shortening with the lowest average myocardial T1 of 135 ± 33 ms and subsequent recovery towards normal values (Figure 4). The differences between the ShMOLLI and MOLLI measurements are shown together with the remaining in-vivo data in Figure 6 and confirm good agreement in-vivo in the low T1 regime predicted in simulation and phantom measurements.

Patients with recent myocardial infarction

As in normal controls, the image acquisition in patients was much faster using ShMOLLI (9.9 ± 1.6 s) than with MOLLI (18.3 ± 3.0 s) without visually perceivable impact on the reconstructed myocardial T1 maps. Figure 7 shows an example of recent transmural inferior infarct 4 days following primary PCI, as demonstrated by LGE at 3T. Table 1 shows T1 values for injured and unaffected normal myocardium measured by distinct T1 distribution peaks in all 4 cases. Unaffected myocardium T1 values showed no significant difference from those of healthy volunteers at 3T measured by ShMOLLI (1216 ± 42 ms, p = 0.09) and MOLLI (1209 ± 35 ms, p = 0.1). Injured myocardium had increased T1 (p < 0.001) of 1432 ± 33 ms (ShMOLLI) and 1396 ± 27 ms (MOLLI). The relative difference in T1 values between injured and unaffected myocardium were 17.8 ± 6% (ShMOLLI) and 15.5 ± 5% (MOLLI), which was statistically different between methods (p < 0.04). The width of identified T1 distribution peaks, indicative of noise penalty, was on average 20% larger for ShMOLLI than for MOLLI (p < 0.02). The area of injured myocardium as identified by T1-maps was on average 4 ± 10% (range -3 to +19%) larger than the area identified by LGE.

The distribution of T1 in case #1 at 3T using A) MOLLI and B) ShMOLLI reveals two distinct populations of values within the myocardium, which can be fitted using two Gaussian curves, corresponding to injured (long T1) and unaffected (normal T1) myocardium. Regions of increased T1 demonstrate spatial co-localisation with LGE 4 days earlier (image insets).

Full size image

Full size table


To exploit benefits of quantitative T1 mapping we sought to develop an accurate practical method characterised by a shorter breath-hold of the order of 10 seconds, which is easily achievable for patients. Figure 8A demonstrates that such drastic shortening of the MOLLI sampling scheme results in significant errors when a straightforward T1 reconstruction [9] is used. The novelty in ShMOLLI approach arises from simulations prior to this study, where we observed that just a single Look-Locker IR is sufficient to accurately estimate long T1 values that are longer than the TRR [18] (Figure 8C). Conversely, inclusion of additional samples from subsequent IRs is essential for the accurate estimation of short T1 values but does not require long epochs for complete recovery of longitudinal magnetisation. Thus, our final design of ShMOLLI is based not only on an abbreviated TI sample set and a drastic reduction of the recovery periods between subsequent inversion recovery experiments. Side effects of such rapid sampling are compensated with a novel concept of conditional data processing to distinguish between long and short T1 relaxation times in order to optimally utilise the available TI samples for accurate non-linear T1 estimation over a wide range of T1 (Figure 8D).

Simulated average relative T1 estimation error (thick lines) with boundaries characterising dependence on heart rate (thin lines) and measurement noise (whiskers) as a function of reference T1ref. A) Standard MOLLI reconstruction applied to the proposed ShMOLLI sampling scheme demonstrates very large errors reaching -60%, with accuracy acceptable only within the shortest T1 range (grey rectangle). B) Accuracy is improved for short T1 range when the last sample is removed from analysis; however very short T1estimates suffer increased variability and the longer T1 are affected by heart rate dependent bias and noise. C) Simple Look-Locker IR experiment has adequate accuracy only for long T1. D) Concept of conditional use of the marked parts of reconstructions A/B/C to obtain the wide range of ShMOLLI T1 estimates with the average bias within 5% range (dotted lines).

Full size image

We used both methods to measure a wide range of T1 applicable to 1.5 and 3T, encompassing values corresponding to contrast enhanced myocardium (T1~150-500 ms), fat (T1~200 ms), liver (~700 ms), skeletal muscle and myocardium (~1000 ms), blood (~2000 ms) and lymph or pericardial effusion (~3000 ms). ShMOLLI demonstrates robust T1 measurement properties across a wide range of T1 values and heart rates in simulation and phantoms. While both ShMOLLI and MOLLI slightly underestimate true T1 values, MOLLI shows prominent dependency on heart rate. An empirical correction for MOLLI has been proposed that addresses specifically the normal myocardial T1 values at 1.5T [3, 10]. In contrast, ShMOLLI underestimation is consistently ~4% for any T1 above 800 ms. This permits easier adjustment to calculate the true T1 values in wide range independently of heart rate:

Excluding any such corrections (as no corrected values are presented in this work), the measurements of myocardial T1 by ShMOLLI at 1.5T are in good agreement with in-vivo data published in the literature [19], particularly with previous measurements using the MOLLI sequence [10]. The observed minor differences between the two methods conform closely to simulation and phantom validation.

The purpose of the limited in-vivo material was to directly compare ShMOLLI performance with MOLLI over a wide range of clinically relevant scenarios. This included measurements in normal myocardium, following shortening of T1's as a result of contrast administration and T1 prolongation related to myocardial infarction. T1 measurements agreed in healthy controls within ± 17 ms noise boundaries for normal myocardium and after Gd administration. Our MI data is consistent with previously published reports [2, 9, 13, 20] showing that the area of infarct demonstrates increased T1 values on non-contrast T1 maps. LGE images were acquired 4-16 days prior to T1-maps, which precludes direct comparisons of infarct size assessment by LGE imaging and T1-mapping. Nonetheless, it is clear that the distribution of T1 values within the myocardium shows two distinct peaks of normal and long T1 s, the latter co-localising to the area of injured myocardium as seen on LGE. ShMOLLI showed a 15% larger T1 difference between injured and unaffected myocardium, which can be directly attributed to its superior quantitation of long T1 s.

Noise considerations

Typically, the penalty for reducing the number of images collected for T1 reconstruction is increased variability within the resulting T1 maps. Our simulation and phantom measurements predicted the noise penalty for ShMOLLI to be as much as 61%. However, in-vivo T1 measurements by ShMOLLI showed noise penalty of only 14% (1.5T) and 18% (3T) as compared to MOLLI T1 s. The repetition error was 1.6% for both methods at 1.5T and only excluding the worst measurements brought out the expected noise advantage of MOLLI at 3T. We found that while MOLLI benefited from more samples for the majority of measurements, the higher SNR was bought at the cost of more prominent outliers, especially at 3T. This may be due to the ShMOLLI conditional processing acting as an error checker, but detailed analysis of such effects exceeds the scope of this work. Overall, for most of the T1 values tested, the in-vivo noise penalty for ShMOLLI due to a shorter imaging time is only 10-20%. We attribute this favourable observation mostly to the beneficial effect of the short breath-hold, which limits the incidence of breathing motion, estimated as the cause for 31% of image artefacts [10]. The small reduction in precision of ShMOLLI T1 maps in-vivo is an excellent trade-off for halving the imaging time and breath-hold, rendering the ShMOLLI both accurate and clinically acceptable.

The ShMOLLI technique holds promise for wider application of non-contrast T1-mapping towards cardiac conditions in which there are focal changes, such as myocardial infarction and myocarditis. It may be pivotal in studying conditions in which there are diffuse myocardial changes, such as chronic heart failure, hypertrophic cardiomyopathy and infiltrative diseases such as cardiac amyloidosis and sarcoidosis. The presented comparisons warrant the ongoing ShMOLLI deployment on its own merit in several more comprehensive patient validation studies.


The novel ShMOLLI sequence for myocardial T1-mapping generates robust, high resolution quantitative T1 maps in agreement with published data in the literature in just 9 heart-beats across a wide range of heart rates and T1 values. Single short breath holds are typical for routine examinations to make them easily achievable for patients and permit wider clinical application of quantitative mapping. Implementation of nonlinear T1 fitting directly in the scanner image reconstruction pipeline yields immediate access to T1-maps for viewing, allowing for re-acquisition if necessary. In patients presenting with recent myocardial infarction, preliminary data demonstrates ShMOLLI superiority in distinguishing injured from normal myocardium, with areas of long T1 co-localizing with injured myocardium as assessed by LGE. ShMOLLI provides a valuable clinically applicable tool for myocardial tissue characterisation with or without the use of contrast agents.


  1. 1.

    Bottomley PA, Foster TH, Argersinger RE, Pfeifer LM: A review of normal tissue hydrogen NMR relaxation times and relaxation mechanisms from 1-100 MHz: dependence on tissue type, NMR frequency, temperature, species, excision, and age. Med Phys. 1984, 11 (4): 425-448. 10.1118/1.595535.

    CASArticlePubMed Google Scholar

  2. 2.

    Messroghli DR, Niendorf T, Schulz-Menger J, Dietz R, Friedrich MG: T1 mapping in patients with acute myocardial infarction. J Cardiovasc Magn Reson. 2003, 5 (2): 353-359. 10.1081/JCMR-120019418.

    ArticlePubMed Google Scholar

  3. 3.

    Messroghli DR, Walters K, Plein S, Sparrow P, Friedrich MG, Ridgway JP, Sivananthan MU: Myocardial T1 mapping: application to patients with acute and chronic myocardial infarction. Magn Reson Med. 2007, 58 (1): 34-40. 10.1002/mrm.21272.

    ArticlePubMed Google Scholar

  4. 4.

    Iles L, Pfluger H, Phrommintikul A, Cherayath J, Aksit P, Gupta SN, Kaye DM, Taylor AJ: Evaluation of diffuse myocardial fibrosis in heart failure with cardiac magnetic resonance contrast-enhanced T1 mapping. J Am Coll Cardiol. 2008, 52 (19): 1574-1580. 10.1016/j.jacc.2008.06.049.

    ArticlePubMed Google Scholar

  5. 5.

    Sparrow P, Messroghli DR, Reid S, Ridgway JP, Bainbridge G, Sivananthan MU: Myocardial T1 mapping for detection of left ventricular myocardial fibrosis in chronic aortic regurgitation: pilot study. AJR Am J Roentgenol. 2006, 187 (6): W630-635. 10.2214/AJR.05.1264.

    ArticlePubMed Google Scholar

  6. 6.

    Maceira AM, Joshi J, Prasad SK, Moon JC, Perugini E, Harding I, Sheppard MN, Poole-Wilson PA, Hawkins PN, Pennell DJ: Cardiovascular magnetic resonance in cardiac amyloidosis. Circulation. 2005, 111 (2): 186-193. 10.1161/01.CIR.0000152819.97857.9D.

    ArticlePubMed Google Scholar

  7. 7.

    Maceira AM, Prasad SK, Hawkins PN, Roughton M, Pennell DJ: Cardiovascular magnetic resonance and prognosis in cardiac amyloidosis. Journal of Cardiovascular Magnetic Resonance. 2008, 10 (1): 10.1186/1532-429X-10-54.

  8. 8.

    Been M, Thomson BJ, Smith MA, Ridgway JP, Douglas RHB, Been M, Best JJK, Muir AL: Myocardial involvement in systemic lupus erythematosus detected by magnetic resonance imaging. European Heart Journal. 1988, 9 (11): 1250-1256.

    CASPubMed Google Scholar

  9. 9.

    Messroghli DR, Radjenovic A, Kozerke S, Higgins DM, Sivananthan MU, Ridgway JP: Modified Look-Locker inversion recovery (MOLLI) for high-resolution T1 mapping of the heart. Magn Reson Med. 2004, 52 (1): 141-146. 10.1002/mrm.20110.

    ArticlePubMed Google Scholar

  10. 10.

    Messroghli DR, Plein S, Higgins DM, Walters K, Jones TR, Ridgway JP, Sivananthan MU: Human myocardium: single-breath-hold MR T1 mapping with high spatial resolution--reproducibility study. Radiology. 2006, 238 (3): 1004-1012. 10.1148/radiol.2382041903.

    ArticlePubMed Google Scholar

  11. 11.

    Marks B, Mitchell DG, Simelaro JP: Breath-holding in healthy and pulmonary-compromised populations: effects of hyperventilation and oxygen inspiration. J Magn Reson Imaging. 1997, 7 (3): 595-597. 10.1002/jmri.1880070323.

    CASArticlePubMed Google Scholar

  12. 12.

    Solano JP, Gomes B, Higginson IJ: A comparison of symptom prevalence in far advanced cancer, AIDS, heart disease, chronic obstructive pulmonary disease and renal disease. J Pain Symptom Manage. 2006, 31 (1): 58-69. 10.1016/j.jpainsymman.2005.06.007.

    ArticlePubMed Google Scholar

  13. 13.

    Messroghli DR, Greiser A, Frohlich M, Dietz R, Schulz-Menger J: Optimization and validation of a fully-integrated pulse sequence for modified look-locker inversion-recovery (MOLLI) T1 mapping of the heart. J Magn Reson Imaging. 2007, 26 (4): 1081-1086. 10.1002/jmri.21119.

    ArticlePubMed Google Scholar

  14. 14.

    Nelder JA, Mead R: A Simplex-Method for Function Minimization. Comput J. 1965, 7 (4): 308-313.

    Article Google Scholar

  15. 15.

    Cochlin L, Blamire A, Styles P: Dependence of T1 and T2 on high field strengths in doped agarose gels; facilitating selection of composition for specific T1/T2 at relevant field. Proceedings of 11th Annual ISMRM Meeting: 2003; Toronto, Canada. 2003, 885-

    Google Scholar

  16. 16.

    Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, Pennell DJ, Rumberger JA, Ryan T, Verani MS: Standardized myocardial sementation and nomenclature for tomographic imaging of the heart: A Statement for Healthcare Professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation. 2002, 105 (4): 539-542. 10.1161/hc0402.102975.

    ArticlePubMed Google Scholar

  17. 17.

    Kim RJ, Wu E, Rafael A, Chen EL, Parker MA, Simonetti O, Klocke FJ, Bonow RO, Judd RM: The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. N Engl J Med. 2000, 343 (20): 1445-1453. 10.1056/NEJM200011163432003.

    CASArticlePubMed Google Scholar

  18. 18.

    Piechnik SK, Ferreira VM, Dall'Armellina E, Cochlin LE, Robson MD: Shortened Modified Look Locker Inversion recovery (Sh-MOLLI) cardiac gated mapping of T1 - Theory and phantom verification. 26th Annual Scientific Meeting, ESMRMB Oct 1-3, 2009 2009; Antalya, Turkey. 2009, 485-

    Google Scholar

  19. 19.

    Sharma P, Socolow J, Patel S, Pettigrew RI, Oshinski JN: Effect of Gd-DTPA-BMA on blood and myocardial T1 at 1.5T and 3T in humans. J Magn Reson Imaging. 2006, 23 (3): 323-330. 10.1002/jmri.20504.

    ArticlePubMed Google Scholar

  20. 20.

    Been M, Ridgeway JP, Douglas RHB: Characterisation of acute myocardial infarction by gated magnetic resonance imaging. Lancet. 1985, 2 (8451): 348-350. 10.1016/S0140-6736(85)92494-8.

    CASArticlePubMed Google Scholar

Download references


This research was funded by the NIHR Oxford Biomedical Research Centre Programme. Dr. Vanessa Ferreira is funded by the Alberta Heritage Foundation for Medical Research (AHFMR) and the University of Oxford Clarendon Fund Scholarship.

Author information


  1. Department of Cardiovascular Medicine, University of Oxford Centre for Clinical Magnetic Resonance Research (OCMR), John Radcliffe Hospital, Oxford, UK

    Stefan K Piechnik, Vanessa M Ferreira, Erica Dall'Armellina, Stefan Neubauer & Matthew D Robson

  2. Stephenson CMR Centre, Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, Canada

    Vanessa M Ferreira

  3. Dept. of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK

    Lowri E Cochlin

  4. Siemens AG Healthcare Sector, Erlangen, Germany

    Andreas Greiser

Corresponding author

Correspondence to Stefan K Piechnik.

Additional information

Competing interests


Authors' contributions

SKP provided the concept and implementation of the method, performed simulations, phantom measurements, data analysis and drafted the manuscript. VMF collected and processed data in normal controls and contributed very significantly to the drafting of the manuscript. EDA collected patient datasets. LEC helped with design of phantoms. AG contributed to the sequence implementation on the scanner. SN contributed to study design acting as the last author from clinical viewpoint. MDR contributed to the study concept, design, method implementation and the manuscript as last author. All authors read, commented or edited the manuscript, and approved the final version.

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Piechnik, S.K., Ferreira, V.M., Dall'Armellina, E. et al. Shortened Modified Look-Locker Inversion recovery (ShMOLLI) for clinical myocardial T1-mapping at 1.5 and 3 T within a 9 heartbeat breathhold. J Cardiovasc Magn Reson12, 69 (2010). https://doi.org/10.1186/1532-429X-12-69

Download citation

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative


  • Cardiovascular Magnetic Resonance
  • Late Gadolinium Enhancement
  • Recent Myocardial Infarction
  • Late Gadolinium Enhancement Image
  • Injured Myocardium
Sours: https://jcmr-online.biomedcentral.com/articles/10.1186/1532-429X-12-69

Shortening t1

Responsive T1 shortening and paraCEST agents

Modulation of CEST by T1 relaxation

Chemical exchange saturation transfer (CEST) agents alter MR image contrast by transferring selectively saturated 1H spins from a small proton pool into the bulk water pool.

The lifetime of the bound water molecule in Eu3+-based PARACEST agents has been found to be quite sensitive to the electronic effects of the ligand pendant arms and alterations in the coordination geometry. The CEST effect in these systems also depends on the longitudinal relaxation time (T1) of the bulk water pool and this can be used to modulate the CEST signal.

We recently reported a Eu3+ (DOTA-tetraamide) complex containing two nitroxide free-radical groups. The nitroxide groups shortened the T1 of the bulk water protons which, in turn, quenched CEST signal originating from the Eu3+-bound water. Reduction of paramagnetic nitroxide moieties by L-ascorbate to a diamagnetic species “turns on” CEST.

Diagram of L-ascorbic acid and Eu<sup>3+</sup>-DOTA tetraamine

 The CEST signal “turns on” on after in vivo reduction of the nitroxide radicals.

Eu2+/3+DOTA(gly)4: a bridge between T1 shortening and paraCEST agents

T1 shortening agents such as paramagnetic metal salts (Gd3+ and Mn2+ complexes) influence image contrast by shortening the longitudinal (T1) and transversal (T2) relaxation times of water protons. Eu2+ is isoelectronic with Gd3+ (both have seven unpaired electrons in an 8S7/2 ground state) but Eu2+ complexes have faster water exchange kinetics than the corresponding Gd3+ complexes.

Most Eu2+ polyamino polycarboxylate complexes have a redox potential significantly lower than that of the Eu2+ aqua ion; Eu2+ complexes have been proposed as redox-sensitive T1 shortening agents because oxidation of Eu2+ yields weakly paramagnetic Eu3+.

Recently we have reported the Eu2+ complex of DOTA(gly)4 has different contrast-enhancing properties depending on the oxidation state of the metal. In its divalent form it is an efficient T1 shortening agent, but oxidation converts it into Eu3+DOTA(gly)4, which is a commonly used paraCEST agent.

Diagram of Eu3+DOTA and MRI scans

The T1 shortening effect diminishes while the CEST effect increases as Eu2+ is being oxidized to Eu3+in vivo.

Sours: https://www.utsouthwestern.edu/labs/kovacs/research/t1-shortening.html
Regulacja łanu pszenicy i pierwszy zabieg fungicydowy T1

Cardiac T1 Mapping and Extracellular Volume (ECV) in clinical practice: a comprehensive review

Journal of Cardiovascular Magnetic Resonancevolume 18, Article number: 89 (2017) Cite this article

  • 52k Accesses

  • 250 Citations

  • 45 Altmetric

  • Metrics details


Cardiovascular Magnetic Resonance is increasingly used to differentiate the aetiology of cardiomyopathies. Late Gadolinium Enhancement (LGE) is the reference standard for non-invasive imaging of myocardial scar and focal fibrosis and is valuable in the differential diagnosis of ischaemic versus non-ischaemic cardiomyopathy. Diffuse fibrosis may go undetected on LGE imaging. Tissue characterisation with parametric mapping methods has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by LGE. Native and post-contrast T1 mapping in particular has shown promise as a novel biomarker to support diagnostic, therapeutic and prognostic decision making in ischaemic and non-ischaemic cardiomyopathies as well as in patients with acute chest pain syndromes. Furthermore, changes in the myocardium over time may be assessed longitudinally with this non-invasive tissue characterisation method.


Cardiovascular Magnetic Resonance (CMR) is increasingly used to differentiate the aetiology of cardiomyopathies. Its three-dimensional nature with excellent spatial resolution and high tissue contrast enables accurate measurement of cardiac function and morphology: left ventricular volumes, mass and ejection fraction as well as an assessment of regional wall motion abnormalities can be achieved largely independent of body habitus, imaging windows and without ionising radiation exposure [1]. Recent advances in CMR provide the potential to also assess and quantify myocardial tissue composition [2]. This article aims to review and illustrate advances in parametric mapping methods, in particular T1 mapping in cardiac diseases and to appraise their clinical potential in the context of established CMR methods.

Late gadolinium enhancement

Late Gadolinium Enhancement (LGE) has become the reference standard for non-invasive imaging of myocardial scar and focal fibrosis in both ischaemic [3] and non-ischaemic cardiomyopathy [4]. LGE imaging depicts the relative difference in longitudinal recovery times (T1) between enhancing areas of fibrosis or scar (T1 shortened due to accumulation of extracellular gadolinium contrast agent) and normal nulled myocardium (longer T1 as gadolinium contrast agent is more rapidly washed out) [2]. The method has particular value in the differential diagnosis of ischaemic versus non-ischaemic cardiomyopathy based on the location and transmural extent of scar. Based upon specific LGE patterns some of the non-ischaemic cardiomyopathies can be further differentiated. Diffuse fibrosis can go undetected on LGE imaging because of the absence of normal reference myocardium and the identification of microscopic interstitial fibrosis is limited by the spatial resolution of LGE images. In the setting of diffuse fibrosis, presence of LGE has been shown to correlate poorly with collagen volume calculated from endomyocardial biopsies [2]. Although numerous quantification methods for LGE exist, the presence of fibrosis and scarring is generally identified qualitatively by visual interpretation of LGE images, limiting the ability to compare findings between subjects or in follow-up examinations.

Principles of T1 mapping

T1 mapping measures the longitudinal or spin-lattice relaxation time, which is determined by how rapidly protons re-equilibrate their spins after being excited by a radiofrequency pulse. In 1970, Look and Locker proposed methods to measure T1 relaxation times by acquiring data successively after magnetisation inversion [5]. Subsequently, these methods have been refined and acquisition times shortened. The Modified Look-Locker Inversion recovery (MOLLI) pulse sequence allows measurement of T1 times in a single breath hold over 17 successive heart beats and has become the most popular T1 mapping method [6]. The main difference between conventional Look-Locker and MOLLI is that in the latter the images are acquired at the same cardiac phase allowing mapping. Variations of MOLLI have been proposed allowing shortened breath-hold durations and reduced sensitivity to heart rate, such as the 5(3)3 scheme indicated in Fig. 1. The Shortened MOLLI (ShMOLLI) scheme uses sequential inversion-recovery measurements with a single breath hold of only nine successive heart beats [7] and a conditional fitting algorithm to account for the short recovery period between inversion pulses. Other pulse sequences including saturation recovery single-shot acquisition (SASHA) [8] and saturation pulse prepared heart-rate-independent inversion recovery (SAPPHIRE) [9] are also used in clinical practice.

Modified Look-Locker Inversion Recovery (MOLLI) scheme for T1-mapping in the heart. This protocol employs two inversions to acquire eight images over 11 heart beats, referred to here as 5(3)3, which means five images are acquired over consecutive cardiac cycles followed by a three heart beat gap and then three images are acquired over consecutive cardiac cycles. 5s(3s)3s MOLLI schemes would acquire images for a duration of 5s followed by a gap of 3s and a second acquisition train lasting 3s, further minimizing heart rate dependency of the results. For illustrative purpose, the orange arrow and relaxation curve refer to an area of myocardial infarction and elevated native T1 values. The green arrow and relaxation curve refer to an area of normal septal myocardium and normal native T1 values. Images are sorted by inversion times

Full size image

T1 mapping refers to pixelwise illustrations of absolute T1 relaxation times on a map. T1 mapping circumvents the influence of windowing and nulling (as in LGE) and allows direct T1 quantification. As such, T1 mapping has the potential to detect diffuse myocardial structural alterations not assessable by other non-invasive means, including LGE.

Currently used T1 mapping methods acquire a set of non-segmented raw images within separate cardiac cycles of a single breath-hold. As a result, the acquisition duration for each raw image is limited to approximately 200 ms within the cardiac cycle, which limits the spatial resolution that can be achieved. In addition, poor breath-holding can significantly impair the quality of T1 maps, which can be compensated for to some extent by the application of manual or automatic motion correction. The differences in acquisition schemes have a direct effect on the range of normal and abnormal T1 with a given technique [10], which means that absolute T1 values can only be directly compared when they were obtained with the same acquisition scheme at the same field strength using the same post-processing methods. Thus, reports on T1 values should always include the T1 mapping technique that was used and the site-specific normal range for T1 [11]. Motion correction is essential for high quality T1 mapping and is generally achieved with breath holding. Image quality can be improved with respiratory motion compensation methods in patients with poor breath-holding [12] and phase sensitive inversion recovery reconstruction [13] further improved image quality. Nevertheless, residual uncorrected respiratory motion is still problematic particularly if unrecognized and in areas of thin myocardium [14].

Native T1 mapping

Native T1 values are primarily influenced by the field strength used, with higher native T1 values at 3 T than at 1.5 T [15]. Measured T1 values also depend on the pulse sequence used (MOLLI and ShMOLLI generally underestimate T1), the cardiac phase (diastole versus systole) and region of measurement [15]. Normal native T1 values are thus specific to the local set-up [16] and need to be reassessed when the acquisition method is changed. Any of the currently used pulse sequence schemes have demonstrated very high inter-study reproducibility for native myocardial T1.

The two most important biological determinants of an increase in native T1 are oedema (increase of tissue water in e.g. acute infarction of inflammation) and increase of interstitial space (e.g. fibrosis of infarction (scar) or cardiomyopathy, and in amyloid deposition). The two most important determinants of low native T1 values are lipid overload (e.g. Anderson-Fabry disease, lipomatous metaplasia in chronic myocardial infarction) and iron overload. Native T1 values are a composite signal of myocytes and extracellular volume (ECV) with the potential of pseudonormalization of abnormal values (e.g. low native T1 values of Anderson-Fabry disease cancelled out by inferolateral fibrosis). Native T1 mapping is feasible even in patients with severe renal impairment in whom gadolinium-based contrast agents are contraindicated.

Contrast-enhanced T1 Mapping and Extracellular Volume (ECV) fraction

Contrast-enhanced T1 mapping is used for mostly calculating the ECV fraction in combination with native T1 mapping. Standard gadolinium-based contrast agents are distributed throughout the extracellular space and shorten T1 relaxation times of myocardium proportional to the local concentration of gadolinium [2]. Areas of fibrosis and scar will therefore exhibit shorter T1 relaxation times, in particular after contrast administration. The haematocrit represents the cellular fraction of blood. Estimation of the ECV (interstitium and extracellular matrix) requires measurement of myocardial and blood T1 before and after administration of contrast agents as well as the patient’s haematocrit value according to the formula:

$$ ECV=\left(1- haematocrit\right)\frac{\frac{1}{post\ contrast\ T1\ myo} - \frac{1}{native\ T1\ myo}}{\frac{1}{post\ contrast\ T1\ blood} - \frac{1}{native\ T1\ blood}} $$

ECV is a marker of myocardial tissue remodelling and provides a physiologically intuitive unit of measurement. Normal ECV values of 25.3 ± 3.5% [1.5 T] have been reported in healthy individuals [17] (Fig. 2). Apart from amyloid, an increased ECV is most often due to excessive collagen deposition and is thus a more robust measure of myocardial fibrosis. Low ECV values occur in thrombus and fat/lipomatous metaplasia. ECV can either be calculated for myocardial regions-of-interest or visualized on ECV maps.

Tissue characterisation using native T1 and extracellular volume fraction (ECV). Absolute values for native T1 depend greatly on field strength (1.5 T or 3 T), pulse sequence (MOLLI or ShMOLLI), scanner manufacturer and rules of measurements. For the purpose of comparability, only studies using 1.5 T scanners were considered in this figure. Figure adapted from Martin Ugander (SCMR 2014)

Full size image

Unlike native T1 relaxation times, contrast-enhanced T1 values are more variable and dependent on contrast agent dosing, the time elapsed between contrast agent administration and T1 measurement and renal clearance. ECV on the other hand represents a physiological parameter and is derived from the ratio of T1 signal values. ECV values may therefore be more reproducible between different field strengths, vendors and acquisition techniques than both native and post-contrast T1 [11]. ECV measures also exhibit better agreement with histological measures of the collagen volume fraction than isolated post-contrast T1 [18].

Clinical use of T1 mapping and ECV

Acute chest pain syndromes

Native T1 and ECV help in the differential diagnosis of patients with acute chest pain including acute coronary syndrome, myocarditis and Takotsubo cardiomyopathy and can help in the distinction of acute from chronic infarction (Fig. 3).

Acute chest pain syndromes algorithm using multi-parametric tissue characterisation. ECV denotes extra-cellular volume, LGE Late Gadolinium Enhancement, and MVO microvascular obstruction. . *This holds true for classical type 1 Takotsubo Cardiomyopathy

Full size image

Acute myocarditis

Endomyocardial biopsy is still the gold standard for confirmation of myocarditis but remains limited by frequent sampling errors reducing diagnostic yield and its invasiveness. In clinical practice, clinical history, laboratory analyses and imaging findings are therefore generally used to diagnose acute myocarditis. The “Lake-Louise” CMR [19] criteria have been widely used to diagnose myocarditis: the diagnosis is likely if two of the three criteria myocardial oedema (T2-weighted imaging), LGE in a mid-wall non-coronary pattern often in the infero-lateral wall, and hyperaemia/capillary leak (increased early gadolinium enhancement ratio between myocardium and skeletal muscle) are present. Radunski et al. have performed a comprehensive comparison of the diagnostic accuracy of conventional CMR techniques and novel mapping techniques and demonstrated better diagnostic accuracy of T1 mapping and in particular by ECV [20] (Fig. 4a). Both T1 and ECV mapping allow for more sensitive identification and quantification of diffuse myocardial fibrosis and oedema than LGE. LGE together with ECV quantification (ECV ≥27% as diagnostic criterion) significantly improved the diagnostic accuracy to 90% (95% CI: 84–95%) compared with 79% (95% CI: 71–85%; p = 0.0043) for the “Lake-Louise” CMR criteria [19]. In patients with severe myocarditis (new-onset heart failure or acute chest pain) raised native T1 (1098 ± 41 ms [1.5T]) and ECV (31 ± 3% [1.5T]) have been reported [20] (Fig. 2). High diagnostic performance (~90% overall sensitivity, specificity and diagnostic accuracy) has been reported for detecting changes in myocarditis using an absolute T1 cut-off of 990 ms [21]. The cut-off value proposed in this study is however specific to the field strength, vendor and T1 mapping technique used and cannot be universally applied.

Multi-parametric tissue characterisation at mid-slice in acute chest pain syndromes. On ECV-maps, red areas represent ECV greater than 30%. T1-mapping was done using a modified Look-Locker Inversion Recovery (MOLLI) pulse sequence on 1.5 Tesla Ingenia, Philips, Best, The Netherlands. a Acute myocarditis with higher native T1-values in the infero-lateral wall of the left ventricle (a1) consistent with LGE in the mid inferior-lateral wall (a2, yellow arrow). The ECV map (a3) demonstrates diffusely increased extra-cellular space. b Takotsubo Cardiomyopathy (TC) with diffusely high native T1 values (b1), no evidence of focal LGE (b2) and diffusely increased ECV (b3). c Acute re-perfused ST-elevation myocardial infarction affecting the inferior wall. Native T1-vales are raised in the area of risk (>1000ms) and also in the remote myocardium. On LGE imaging, inferior infarction with an area of microvascular obstruction can be seen (yellow arrow, c2). ECV is raised in the infarct zone but low in the MVO as this area does not take up any contract (yellow arrow, c3). d Anterior wall ST-elevation myocardial infarction with rupture of the left ventricle free wall (not seen in these images) resulting in haemo-pericardium. The pericardial haemorrhage has high native T1 values (black arrow, d1), high signal on LGE and low ECV values (d3). e Chronic MI in the antero-septal wall. There is an area of reduced native T1 values in the septum (green arrow, e1) which corresponds to lipomatous metaplasia transformation in previous antero-septal infarct. There is also an acute infarction in the lateral wall with some peri-infarct oedema seen on native T1. Abbreviations: AMI, acute myocardial infarction; ECV, extra-cellular volume; MI, myocardial infarction; LGE, Late Gadolinium Enhancement; TC, Takotsubo Cardiomyopathy

Full size image

Takotsubo cardiomyopathy

Acute but rapidly reversible mid and apical left ventricular (LV) segment akinesia with ballooning and compensatory hyperkinesia of basal segments is the typical finding in Takotsubo or stress-induced cardiomyopathy. Typically there are no perfusion defects and no scar on LGE imaging in contrast to myocarditis and myocardial infarction. In current clinical practice, T2-weighted imaging using the short-tau inversion recovery (STIR) sequence is used to detect oedema. T1 mapping has potential advantages over T2 STIR as it is a quantitative method that is not in need of a reference region of interest (ROI) and it can be obtained in a single breath hold. Ferreira et al. demonstrated elevated native T1 values with a good correlation between T1 values and T2 signal intensity (SI) ratios and high diagnostic accuracy (AUC = 0.94, sensitivity and specificity of 92%) in the differentiation between oedema and normal myocardium [22] (Figs. 2 and 4b). Elevated native T1 values can both be caused by myocardial oedema and fibrosis. In clinical practice, a presumable myocardial oedema zone in Takotsubo cardiomyopathy might be further substantiated with T2-weighted imaging such as STIR sequences or T2 mapping sequences. The presence of fibrosis, as may exist if the patient has another underlying pathology, may cause an increased ECV.

Acute myocardial infarction

Ischaemia triggers the development of cellular oedema. Native T1 reliably detects segmental abnormalities caused by acute myocardial infarction (MI) with high sensitivity and specificity [23]. T1 mapping detects myocardial oedema in both ST-elevation MI (STEMI) and non ST-elevation MI (NSTEMI) patients [24] and is at least as sensitive as T2-STIR [22], in particular in patients with smaller infarcts [23] (Fig. 4c, d). Although the distinction of acute vs. chronic MI can be challenging, T1 values in acute MI are generally higher than in chronic MI and thus may allow distinction of an acute coronary syndrome (ACS) from chronic injury. Prescribing a distinct cut-off value that can be used in an individual patient is hampered by the general variability of native T1 values between subjects, the influence of field strength and acquisition pulse sequence on T1 values and the influence of infarct size on T1 values. In practice, the distinction of acute vs. chronic MI remains mainly based on the overall assessment of infarct, oedema (area at risk), and microvascular obstruction zone.

Furthermore, T1 values progress from normal myocardium to that of maximal injury and can be used for defining the peri-infarct zone/area-at-risk [24]. The longitudinal relaxation time measured by T1 mapping is mainly related to tissue fibrosis and oedema. Carrick et al. [25] have shown that an infarct core with native T1 values lower than the surrounding area at risk correlated with the microvascular obstruction zone by contrast-enhanced CMR and was associated with worse clinical outcome.

A recent study of 300 patients with reperfused STEMI demonstrated native T1 remote from infarcted myocardium at baseline to be independently predictive of adverse LV remodelling and adverse cardiac events 6 months post-STEMI [26]. Native T1 values in acute MI are high and ECV values are among the highest of all cardiac disease (58.5 ± 7.6) [17], most likely due to disruption of cardiomyocyte membrane integrity and subsequent expansion of the distribution volume of extracellular contrast agents (Fig. 2).

Microvascular obstruction in the infarct core (no-reflow phenomenon) results in a pseudo-normalization of T1 values in this area [23, 24]. Due to accumulation of methaemoglobin (T1 shortening effect), T1 can even be decreased in the case of intramyocardial haemorrhage (Fig. 4c).

Native T1 mapping might also be useful in the assessment of complication of AMI as illustrated in Fig. 4d showing haemo-pericardium with high native T1 values in a patient with ruptured LV free wall.

Chronic myocardial infarction

In chronic MI, the necrotic and oedematous infarct tissue of an acute infarct is replaced by a smaller area of increased extracellular collagen (fibrous scar). Native T1 values are therefore lower and less extensive in chronic MI compared with the acute stage. The ECV of chronically infarcted myocardium has been shown to be markedly elevated (51 ± 8%) compared to normal myocardium but slightly lower than in acutely infarcted myocardium (Figs. 2 and 4e) [27].

T1 mapping is also able to illustrate areas of lipomatous metaplasia in chronic MI, the presence of which alters the electrical properties of the myocardium and might play a role in post-MI arrhythmogenesis [28]. Fat has very low T1 values (230–350 ms at 1.5 T) [29] and the fatty replacement area within the infarct core therefore displays noticeable T1 decrease [24].

Non-ischaemic cardiomyopathies

Cardiac amyloidosis

Amyloidosis can be regarded as the exemplar of an interstitial disease. Although endomyocardial biopsy remains the reference standard for diagnosis, it is not routinely performed because it is invasive and prone to sampling errors with false-negative results. The typical constellation on echocardiography (concentric LV hypertrophy, bi-atrial dilatation, restrictive filling), ECG (low-voltage QRS in spite of LV hypertrophy) and elevated blood biomarkers (cardiac troponin and natriuretic peptides) is found mainly in advanced disease. Myocardial amyloid deposition results in interstitial expansion, which can be visualized by typically patchy or subendocardial LGE with early blood pool darkening on Look Locker scout images. The characteristic diffuse LGE enhancement though makes nulling of normal myocardium particularly difficult, often leading to confusion in interpretation [30].

T1 mapping circumvents the limitations of myocardial nulling faced in LGE imaging, provides quantitative assessment of diffuse extracellular expansion, and is a viable option in renal failure, which is common with amyloid. Performed serially, it might be a means to follow response to treatment and changes in myocardial burden [31]. Both types of cardiac amyloidosis show markedly elevated native T1 values (Figs. 2 and 5a). Using native T1 cardiac amyloidosis could be reliably diagnosed and differentiated from hypertrophic cardiomyopathy, a clinically relevant differential diagnosis [32].

Multi-parametric tissue characterisation at mid-slice in diseases involving myocardium. On ECV-maps, red areas represent ECV greater than 30%. T1-mapping was done using a modified Look-Locker Inversion Recovery (MOLLI) pulse sequence on 1.5 Tesla Ingenia, Philips, Best, The Netherlands. a Biopsy proven cardiac amyloidosis. T1 maps show diffuse rise in native-T1 values (a1). On LGE-imaging, there is low contrast-noise ratio (CNR) between the blood pool and the myocardium (a2). ECV-maps demonstrate diffuse rise in extra-cellular space in the whole myocardium. b Established rheumatoid arthritis demonstrating some rise in native T1 (b1) and ECV (b3) with normal signal distribution on LGE-imaging (b2). c Established Systemic Sclerosis demonstrating rise in native T1 values predominantly in the septum (c1) and more widespread increase in ECV (c3). There is no evidence of any scar or fibrosis on LGE-imaging. d Bio-chemical diagnosis of Fabry’s disease: Native T1 (e1) demonstrates pseudo-normalization due to the effects of replacement fibrosis exceeding the fatty-related T1 decrease. LGE (e2) demonstrates fibrosis of the lateral wall in consistence with the ECV map (e3). Abbreviations: ECV, extra-cellular volume; LGE, Late Gadolinium Enhancement

Full size image

Cardiac amyloid is associated with a higher ECV than any other cardiomyopathy (ECV 46.6 ± 7.0%) due to the widespread and substantial extracellular infiltration [17].

Early detection of cardiac amyloidosis and differentiation between the two main forms transthyretin-related cardiac amyloidosis (ATTR) and light chains cardiac amyloidosis (AL) is of high clinical importance because untreated cardiac amyloidosis has poor prognosis. In addition to supportive heart failure therapy, specific treatment options are available for both ATTR (liver transplantation, novel TTR-specific treatment) and AL (chemotherapy, autologous stem cell transplantation) [33]. Efforts to differentiate between ATTR and AL have been made by Dungu et al., who reported higher LV mass in patients with ATTR compared to AL and proposed a sum LGE score (QALE score) for differential diagnosis [34]: LGE patterns seem to be more extensive, diffuse and transmural in ATTR (QALE score ≥13) and more often showing a less extensive, more subendocardial pattern in AL cardiac amyloidosis (QALE score <13) [34]. The overlap between AL and ATTR amyloidosis, though, remains substantial. Given the great therapeutic consequence of ATTR (liver transplantation, novel TTR-specific treatment) vs. AL amyloidosis (chemotherapy, autologous stem cell transplantation) currently further testing is required with cardiac biopsy, genetic testing, or nuclear scanning to confidently distinguish between the two disease types.

Nevertheless ECV has been proposed to become the first non-invasive test to quantify cardiac amyloid burden and could be used as a tool to guide and monitor treatment [35].

Systemic cardiac disorders

Rheumatoid arthritis

Subclinical cardiovascular disease is common in patients with rheumatoid arthritis (RA) and predominantly affects young female subjects. Up to 39% of RA patients have been reported to show focal LGE patterns, probably related to earlier myocarditis [36]. However, diffuse fibrosis is common in RA and cannot be reliably detected by LGE. In a pilot study, native T1 values were slightly elevated in RA patients compared to controls and RA patients had expanded ECV (30.3 ± 3.4 vs. 27.9 ± 2.0; p < 0.001) [37] (Figs. 2 and 5b). Disease activity scores correlated with diffuse fibrosis and systolic and diastolic strain regardless of LGE [37].

Systemic sclerosis

Cardiac involvement is common in systemic sclerosis, often before cardiac symptoms occur. Low grade inflammation and diffuse myocardial fibrosis are well-described co-existing disease processes in systemic sclerosis and can be detected by T2-STIR and LGE imaging. As in other diseases, LGE is limited in the assessment of diffuse myocardial fibrosis, especially when the entire myocardium may be affected more homogeneously as occurs with systemic sclerosis. In a small study, native T1 and ECV (35.4 ± 4.8 vs. 27.6 ± 2.5%) were elevated in patients with systemic sclerosis (Figs. 2 and 5c) [38].

Anderson-fabry disease

Anderson-Fabry disease (AFD) is an intracellular lipid disorder (lysosomal storage disease) that causes concentric LV hypertrophy, heart failure and arrhythmias [39].

On LGE images, AFD typically displays an infero-lateral mid-wall pattern of enhancement caused by focal fibrosis in this region. In addition, the low native T1 of fat can serve as an early surrogate marker of myocardial glycosphingolipid storage in AFD even before the development of LV hypertrophy [40]. Native T1 reliably distinguished AFD from other common causes of LV hypertrophy using a predefined cut-off [40]. However, segmental T1 analysis in the infero-lateral wall showed pseudo-normalized or even elevated T1 due to the effects of replacement fibrosis exceeding the fatty-related T1 decrease [40]. Unlike native T1, the ECV in AFD is typically normal as AFD is an intracellular (lysosomal) storage disease [17] and ECV values in AFD have been reported to be similar to healthy controls (ECV 21.7 ± 3.0% [1.5T]) [41] (Figs. 2 and 5d).

Iron-overload cardiomyopathy

Iron-overload develops primarily from increased absorption such as in genetic hemochromatosis or secondary to repeated blood transfusions, as in thalassaemia major [42]. Cardiac iron deposition confers a poor prognosis without (chelation) therapy [42]. Iron as a ferromagnetic material is known to shorten the three fundamental tissue MRI signal constants, T1, T2 and T2*. T2* currently is the non-invasive gold standard method to quantify iron deposition in myocardium [43]. Sado et al. have shown shown that native T1 values were lower in patients with iron-overload cardiomyopathy with good correlation with T2* [44] (Fig. 2). Compared with T2* mapping, T1 has the advantage of higher reproducibility, easier clinical use with less offline analysis needed and the potential to detect early iron overload [44]. On the other hand, unlike T2*, T1 is less pathologically specific and increased in various other cardiomyopathies involving increased interstitial space (fibrosis, amyloid). Therefore, early iron deposition may be missed in these patients.

Diffuse fibrosis

Hypertrophic cardiomyopathy

Autosomal dominant mutations involving sarcomeric genes lead to hypertrophic cardiomyopathy (HCM) and a combination of myocyte disarray, fibrosis and ventricular hypertrophy in distinct patterns [45]. Clinically, HCM is diagnosed by a combination of history (pedigree), ECG signs and an evaluation of LV wall thickness. LGE typically occurs at right ventricular (RV) insertion points and with variable frequency and severity in hypertrophied, often hypocontractile segments [45]. Histologically, fibrosis is often more global, or diffuse, and often undetectable by standard LGE pulse sequences (nulled reference tissue potentially in area of diffuse fibrosis). Native T1 values are prolonged in HCM and correlate with wall thickness suggesting that it is a marker of disease severity [46, 47]. Patients with HCM have reduced post-contrast myocardial T1 consistent with the presence of diffuse interstitial fibrosis outside areas of LGE. ECV in HCM (29.1 ± 0.5% [1.5T]) [17] in segments without LGE has shown to be in the upper normal range of normal patients (Figs. 2 and 6a) [48]. ECV can be used in the differential diagnosis of HCM vs. athletic remodelling in athlete’s heart, in particular in those subjects in the grey zone of LV wall thickness (12–15 mm). Whereas ECV increases with increasing LV hypertrophy in HCM (due to extracellular matrix expansion and myocardial disarray), ECV reduces in athletes with increasing wall thickness (due to an increase in healthy myocardium by cellular hypertrophy) [48]. The impact of myocardial disarray on T1 mapping in HCM, though, remains controversial and may result in overestimation of ECV [49].

Multi-parametric tissue characterisation at mid-slice in cardiomyopathies. On ECV-maps, red areas represent ECV greater than 30%. T1-mapping was done using a modified Look-Locker Inversion Recovery (MOLLI) pulse sequence on 1.5 Tesla Ingenia, Philips, Best, The Netherlands. a HCM showing diffuse and heterogeneous LGE in the anterior wall (yellow arrow, a2). Native T1 was diffusely raised, exceeding the hypertrophied segments (a1). ECV-maps demonstrate higher ECV in and around the diffuse LGE (a3). b DCM with no LGE enhancement (b2) but raised native T1 values in the septum (1000–1200ms) (b1) and raised ECV (b3). c HFpEF Native-T1 values were significantly raised through-out (>1000ms) with no presence of scar on LGE-imaging (c2). ECV maps demonstrated patchy rise in extra-cellular space (c3). Abbreviations: DCM, dilated cardiomyopathy; ECV, extra-cellular volume; HFpEF, heart failure with preserved ejection fraction; HCM, hypertrophic cardiomyopathy; LGE, Late Gadolinium Enhancement

Full size image

Dilated cardiomyopathy

LV or biventricular dilatation and systolic dysfunction without an obvious or detectable cause are the defining characteristics of dilated (nonischaemic) cardiomyopathy (DCM). In DCM, LGE typically occurs in a mid-wall pattern [50] but in the majority of DCM there is lack of any detectable LGE. Native T1 values are prolonged in DCM and correlate with reduced wall thickness [46, 47]. ECV measurement reflects myocardial collagen content in DCM and might serve as a non-invasive imaging biomarker to monitor therapy response and aid risk stratification in different stages of DCM [51]. ECV in DCM has been shown to be in a similar range to HCM (28 ± 0.4% [1.5T]) [17] (Figs. 2 and 6b). The pathophysiologic correlates that are responsible for the similar ECV values in DCM and HCM are not fully understood, but since DCM and HCM can usually be distinguished by their distinct ventricular geometry the overlap in ECV is clinically irrelevant. Furthermore, ECV elevation is typically pronounced in the mid-wall sections in DCM compared with RV hinge points and hypertrophied segments in HCM.

Heart failure and heart failure with preserved ejection fraction

Heart failure is the final common pathway of many cardiomyopathies. Myocardial fibrosis – regardless of the aetiology – is a key mechanism in the development of diastolic and systolic heart failure. Since collagen deposition is often diffuse, LGE usually shows no regional fibrosis/scarring. According to data from the OPTIMIZE-HF registry preserved ejection fraction was present in a large proportion of patients with heart failure. Both heart failure patients with reduced and preserved ejection fraction experienced similar rates of mortality and morbidity [52]. Su et al. have shown that patients with systolic heart failure and heart failure with preserved ejection fraction (HFpEF) had elevated ECV in comparison with normal control subjects (31.2% vs. 28.9% vs. 27.9%) [53] (Fig. 6c). ECV elevation thus might help in identifying patients with worse prognosis otherwise undetected by conventional LGE techniques.


Tissue characterisation by native T1 mapping may serve as an important source of diagnostic, therapeutic and prognostic decision making in various cardiac diseases.

An advantage of a non-invasive method for the assessment of fibrosis is the potential to follow changes in the myocardium over time as in patients with cardiomyopathies or patients receiving cardiotoxic drugs. Patients with poor renal function (or on dialysis) precluding gadolinium-based contrast injection may benefit from using native T1 mapping instead of LGE imaging. A clinical scenario where multi-parametric CMR tissue characterisation has already been established is in the assessment of patients with acute chest pain and no coronary artery disease. In such cases, tissue characterisation can assist in the differential diagnosis of micro-infarction, (peri-) myocarditis and stress cardiomyopathy (Takotsubo) and also other causes of diffuse fibrosis associated with high cardiac biomarker levels (such as high-sensitivity cardiac troponin).

Clinically, several studies have shown that T1 mapping with ECV is particularly useful in the assessment of cardiac diseases with diffuse fibrosis. Furthermore, T1 mapping with ECV might be helpful as an adjunct in cases with ambiguous LGE. Beyond differential diagnosis of cardiomyopathies, tissue characterization with T1 mapping can be very useful in differentiating between pericardial fat vs. LGE, differentiating between epicardial fat vs. pericardial effusion as well as in tissue characterization of various cardiac tumours.

More research is needed regarding the long-term prognostic impact of T1/ECV mapping, as well as its potential in therapy guidance of cardiac diseases such as heart failure, patients after heart transplantation as well as its role in valvular heart disease.

Harmonization of acquisition protocols between vendors and institution will also be needed to allow wider adoption of the methods.

Although tissue characterisation with native T1 and ECV has been shown to have incremental diagnostic benefit even in very early disease stages (e.g. diffuse fibrosis not detectable by LGE), there is an overlap between different cardiomyopathies and some overlap with normal T1 values. Like all medical parameters, abnormalities in native T1 and ECV need to be interpreted within their clinical context and pre-test probabilities and in conjunction with established CMR techniques such as LGE. Elevations and reductions of T1 and ECV are not specific and can be caused by various disease processes. In some instances, these processes can even cancel each other out (e.g. pseudonormalization in Anderson-Fabry disease when replacement fibrosis exceeds the fatty-related T1 decrease).

There is still some way to go with standardization of T1 mapping methods and protocols. Ongoing research for this purpose includes the use of standardized phantoms and software methods. For now, normal and pathological T1 values will largely depend on the acquisition scheme and will have to be defined in individual CMR centres.


Anderson-Fabry disease

Light chains cardiac amyloidosis

Transthyretin-related cardiac amyloidosis

Cardiovascular magnetic resonance

Dilated cardiomyopathy

Extracellular volume

Hypertrophic cardiomyopathy

Heart failure with preserved ejection fraction

Late gadolinium enhancement

Left ventricular

Modified look-locker inversion recovery

Rheumatoid arthritis

Region of interest

Saturation pulse prepared heart-rate-independent inversion recovery

Saturation recovery single-shot acquisition

Shortened modified look-locker inversion recovery

Signal intensity

Short-tau inversion recovery

Longitudinal recovery time


  1. 1.

    Hundley WG, Bluemke DA, Finn JP, Flamm SD, Fogel MA, Friedrich MG, Ho VB, Jerosch-Herold M, Kramer CM, Manning WJ, Patel M, Pohost GM, Stillman AE, White RD, Woodard PK. ACCF/ACR/AHA/NASCI/SCMR 2010 expert consensus document on cardiovascular magnetic resonance: a report of the american college of cardiology foundation task force on expert consensus documents. J Am Coll Cardiol. 2010;55:2614–62.

    ArticlePubMed Google Scholar

  2. 2.

    Parsai C, O’Hanlon R, Prasad SK, Mohiaddin RH. Diagnostic and prognostic value of cardiovascular magnetic resonance in non-ischaemic cardiomyopathies. J Cardiovasc Magn Reson. 2012;14:54.

    ArticlePubMedPubMed Central Google Scholar

  3. 3.

    Kim RJ, Wu E, Rafael A, Chen EL, Parker MA, Simonetti O, Klocke FJ, Bonow RO, Judd RM. The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. N Engl J Med. 2000;343:1445–53.

    CASArticlePubMed Google Scholar

  4. 4.

    Gulati A, Jabbour A, Ismail TF, Guha K, Khwaja J, Raza S, Morarji K, Brown TD, Ismail NA, Dweck MR, Di Pietro E, Roughton M, Wage R, Daryani Y, O’Hanlon R, Sheppard MN, Alpendurada F, Lyon AR, Cook SA, Cowie MR, Assomull RG, Pennell DJ, Prasad SK. Association of fibrosis with mortality and sudden cardiac death in patients with nonischemic dilated cardiomyopathy. JAMA. 2013;309:896–908.

    CASArticlePubMed Google Scholar

  5. 5.

    Look DC, Locker DR. Time saving in measurement of NMR and EPR relaxation times. Rev Sci Instrum. 1970;41:250–1.

    CASArticle Google Scholar

  6. 6.

    Messroghli DR, Radjenovic A, Kozerke S, Higgins DM, Sivananthan MU, Ridgway JP. Modified Look-Locker inversion recovery (MOLLI) for high-resolution T1 mapping of the heart. Magn Reson Med. 2004;52:141–6.

    ArticlePubMed Google Scholar

  7. 7.

    Piechnik SK, Ferreira VM, Dall’Armellina E, Cochlin LE, Greiser A, Neubauer S, Robson MD. Shortened Modified Look-Locker Inversion recovery (ShMOLLI) for clinical myocardial T1-mapping at 1.5 and 3 T within a 9 heartbeat breathhold. J Cardiovasc Magn Reson. 2010;12:69.

    ArticlePubMedPubMed Central Google Scholar

  8. 8.

    Chow K, Flewitt JA, Green JD, Pagano JJ, Friedrich MG, Thompson RB. Saturation recovery single-shot acquisition (SASHA) for myocardial T(1) mapping. Magn Reson Med. 2014;71:2082–95.

    ArticlePubMed Google Scholar

  9. 9.

    Weingärtner S, Akçakaya M, Basha T, Kissinger KV, Goddu B, Berg S, Manning WJ, Nezafat R. Combined Saturation/Inversion Recovery Sequences for Improved Evaluation of Scar and Diffuse Fibrosis in patients with Arrhythmia or Heart Rate Variability. Magn Reson Med. 2014;71:1024–34.

    ArticlePubMed Google Scholar

  10. 10.

    Kellman P, Hansen MS. T1-mapping in the heart: accuracy and precision. J Cardiovasc Magn Reson. 2014;16:2.

    ArticlePubMedPubMed Central Google Scholar

  11. 11.

    Moon JC, Messroghli DR, Kellman P, Piechnik SK, Robson MD, Ugander M, Gatehouse PD, Arai AE, Friedrich MG, Neubauer S, Schulz-Menger J, Schelbert EB. Myocardial T1 mapping and extracellular volume quantification: a Society for Cardiovascular Magnetic Resonance (SCMR) and CMR Working Group of the European Society of Cardiology consensus statement. J Cardiovasc Magn Reson. 2013;15:92.

    ArticlePubMedPubMed Central Google Scholar

  12. 12.

    Xue H, Shah S, Greiser A, Guetter C, Littmann A, Jolly MP, Arai AE, Zuehlsdorff S, Guehring J, Kellman P. Motion correction for myocardial T1 mapping using image registration with synthetic image estimation. Magn Reson Med. 2012;67:1644–55.

    ArticlePubMed Google Scholar

  13. 13.

    Xue H, Greiser A, Zuehlsdorff S, Jolly MP, Guehring J, Arai AE, Kellman P. Phase-sensitive inversion recovery for myocardial T(1) mapping with motion correction and parametric fitting. Magn Reson Med. 2013;69:1408–20.

    ArticlePubMed Google Scholar

  14. 14.

    Kellman P, Arai AE, Xue H. T1 and extracellular volume mapping in the heart: estimation of error maps and the influence of noise on precision. J Cardiovasc Magn Reson. 2013;15:56.

    ArticlePubMedPubMed Central Google Scholar

  15. 15.

    Kawel N, Nacif M, Zavodni A, Jones J, Liu S, Sibley CT, Bluemke DA. T1 mapping of the myocardium: intra-individual assessment of the effect of field strength, cardiac cycle and variation by myocardial region. J Cardiovasc Magn Reson. 2012;14:27.

    ArticlePubMedPubMed Central Google Scholar

  16. 16.

    Kawel-Boehm N, Maceira A, Valsangiacomo-Buechel ER, Vogel-Claussen J, Turkbey EB, Williams R, Plein S, Tee M, Eng J, Bluemke DA. Normal values for cardiovascular magnetic resonance in adults and children. J Cardiovasc Magn Reson. 2015;17:29.

    ArticlePubMedPubMed Central Google Scholar

  17. 17.

    Sado DM, Flett AS, Banypersad SM, White SK, Maestrini V, Quarta G, Lachmann RH, Murphy E, Mehta A, Hughes DA, McKenna WJ, Taylor AM, Hausenloy DJ, Hawkins PN, Elliott PM, Moon JC. Cardiovascular magnetic resonance measurement of myocardial extracellular volume in health and disease. Heart. 2012;98:1436–41.

    ArticlePubMed Google Scholar

  18. 18.

    Sibley CT, Noureldin RA, Gai N, Nacif MS, Liu S, Turkbey EB, Mudd JO, van der Geest RJ, Lima JA, Halushka MK, Bluemke DA. T1 mapping in cardiomyopathy at cardiac MR: comparison with endomyocardial biopsy. Radiology. 2012;265:724–32.

    ArticlePubMedPubMed Central Google Scholar

  19. 19.

    Friedrich MG, Sechtem U, Schulz-Menger J, Holmvang G, Alakija P, Cooper LT, White JA, Abdel-Aty H, Gutberlet M, Prasad S, Aletras A, Laissy JP, Paterson I, Filipchuk NG, Kumar A, Pauschinger M, Liu P. A JACC White Paper. JACC. 2009;53:1475–87.

    ArticlePubMedPubMed Central Google Scholar

  20. 20.

    Radunski UK, Lund GK, Stehning C, Schnackenburg B, Bohnen S, Adam G, Blankenberg S, Muellerleile K. CMR in patients with severe myocarditis: diagnostic value of quantitative tissue markers including extracellular volume imaging. JACC Cardiovasc Imaging. 2014;7:667–75.

    ArticlePubMed Google Scholar

  21. 21.

    Ferreira VM, Piechnik SK, Dall’Armellina E, Karamitsos TD, Francis JM, Ntusi N, Holloway C, Choudhury RP, Kardos A, Robson MD, Friedrich MG, Neubauer S. T1 mapping for the diagnosis of acute myocarditis using CMR: comparison to T2-weighted and late gadolinium enhanced imaging. J Am Coll Cardiol Img. 2013;6:1048–58.

    Article Google Scholar

  22. 22.

    Ferreira VM, Piechnik SK, Dall’Armellina E, Karamitsos TD, Francis JM, Choudhury RP, Friedrich MG, Robson MD, Neubauer S. Non-contrast T1-mapping detects acute myocardial edema with high diagnostic accuracy: a comparison to T2-weighted cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2012;14:42.

    ArticlePubMedPubMed Central Google Scholar

  23. 23.

    Dall’Armellina E, Piechnik SK, Ferreira VM, Si QL, Robson MD, Francis JM, Cuculi F, Kharbanda RK, Banning AP, Choudhury RP, Karamitsos TD, Neubauer S. Cardiovascular magnetic resonance by non contrast T1-mapping allows assessment of severity of injury in acute myocardial infarction. J Cardiovasc Magn Reson. 2012;14:15.

    ArticlePubMedPubMed Central Google Scholar

  24. 24.

    h-Ici DO, Jeuthe S, Al-Wakeel N, Berger F, Kuehne T, Kozerke S, Messroghli DR. T1 mapping in ischaemic heart disease. Eur Heart J Cardiovasc Imag. 2014;15:597–602.

    Article Google Scholar

  25. 25.

    Carrick D, Haig C, Rauhalammi S, Ahmed N, Mordi I, McEntegart M, Petrie MC, Eteiba H, Hood S, Watkins S, Lindsay M, Mahrous A, Ford I, Tzemos N, Sattar N, Welsh P, Radjenovic A, Oldroyd KG, Berry C. Prognostic significance of infarct core pathology revealed by quantitative non-contrast in comparison with contrast cardiac magnetic resonance imaging in reperfused ST-elevation myocardial infarction survivors. Eur Heart J. 2016;37:1044–59.

    ArticlePubMed Google Scholar

  26. 26.

    Carrick D, Haig C, Rauhalammi S, Ahmed N, Mordi I, McEntegart M, Petrie MC, Eteiba H, Lindsay M, Watkins S, Hood S, Davie A, Mahrous A, Sattar N, Welsh P, Tzemos N, Radjenovic A, Ford I, Oldroyd KG, Berry C. Pathophysiology of LV remodelling in survivors of STEMI: inflammation, remote myocardium, and prognosis. JACC Cardiovasc Imaging. 2015;8:779–90.

    ArticlePubMedPubMed Central Google Scholar

  27. 27.

    Ugander M, Oki AJ, Hsu LY, Kellman P, Greiser A, Aletras AH, Sibley CT, Chen MY, Bandettini WP, Arai AE. Extracellular volume imaging by magnetic resonance imaging provides insights into overt and subclinical myocardial pathology. Eur Heart J. 2012;33:1268–78.

    CASArticlePubMedPubMed Central Google Scholar

  28. 28.

    de Bakker JM, van Capelle FJ, Janse MJ, Wilde AA, Coronel R, Becker AE, Dingemans KP, van Hemel NM, Hauer RN. Reentry as a cause of ventricular tachycardia in patients with chronic ischemic heart disease: electrophysiologic and anatomic correlation. Circulation. 1988;77:589–606.

    ArticlePubMed Google Scholar

  29. 29.

    Ferreira VM, Holloway CJ, Piechnik SK, Karamitsos TD, Neubauer S. Is it really fat? Ask a T1-map. Eur Heart J Cardiovasc Imaging. 2013;14:1060.

    ArticlePubMedPubMed Central Google Scholar

  30. 30.

    Maceira AM, Joshi J, Prasad SK, Moon JC, Perugini E, Harding I, Sheppard MN, Poole-Wilson PA, Hawkins PN, Pennell DJ. Cardiovascular magnetic resonance in cardiac amyloidosis. Circulation. 2005;111:186–93.

    ArticlePubMed Google Scholar

  31. 31.

    Ruberg FL. T1 mapping in cardiac amyloidosis: can we get from here? JACC Cardiovasc Imaging. 2013;6:498–500.

    ArticlePubMed Google Scholar

  32. 32.

    Fontana M, Banypersad SM, Treibel TA, Maestrini V, Sado DM, White SK, Pica S, Castelletti S, Piechnik SK, Robson MD, Gilbertson JA, Rowczenio D, Hutt DF, Lachmann HJ, Wechalekar AD, Whelan CJ, Gillmore JD, Hawkins PN, Moon JC. Native T1 mapping in transthyretin amyloidosis. JACC Cardiovasc Imaging. 2014;7:157–65.

    ArticlePubMed Google Scholar

  33. 33.

    Patel KS, Hawkins PN. Cardiac amyloidosis: where are we today? J Intern Med. 2015;278:126–44.

    CASArticlePubMed Google Scholar

  34. 34.
Sours: https://jcmr-online.biomedcentral.com/articles/10.1186/s12968-016-0308-4

Now discussing:

International Journal
of Molecular

1. Introduction

Magnetic resonance imaging (MRI) contrast agents are widely used to increase the contrast difference between normal and abnormal tissues. Shortly after the introduction of clinical MRI, the first contrast-enhanced human MRI study was reported in 1981 using ferric chloride as the contrast agent in the gastrointestinal (GI) tract (1). In 1984, Carr et al first proved the use of a gadolinium compound as a diagnostic intravascular MRI contrast agent (2). Almost half of the MRI studies performed nowadays are contrast-enhanced studies, and this is a growing trend (3). Newer contrast agents are constantly being discovered and investigated. The safety of contrast agents for clinical use is under strict scrutiny. This review therefore, aims to classify the MRI contrast agents discovered to date into relevant groups and to also discuss their applications, structures, mechanisms of action, pharmacokinetics and pharmacodynamics.

MRI contrast agents may be categorised according to the following features (4): magnetic properties, chemical composition, the presence or absence of metal atoms, route of administration, effect on the magnetic resonance image, biodistribution and application.

2. Magnetic properties

The majority of MRI contrast agents are either paramagnetic gadolinium ion complexes or superparamagnetic (iron oxide) magnetite particles. The paramagnetic contrast agents are usually made from dysprosium (Dy3+), the lanthanide metal gadolinium (Gd3+), or the transition metal manganese (Mn2+) and possess water soluble properties. The most commonly selected metal atom used in MRI contrast agents is the lanthanide ion gadolinium (III) as it possesses a high magnetic moment and it is the most stable ion with unpaired electrons. Due to the presence of unpaired electrons, these contrast agents possess paramagnetic properties; gadolinium has seven, dysprosium has four and manganese has five unpaired electrons. Contrast agents containing gadolinium shorten the T1 (or longitudinal) and T2 (or transverse) relaxation time of neighbouring water protons (Fig. 1). These effects increase the signal intensity of T1-weighted images, and reduce the signal intensity of T2-weighted images (5,6). T1 shortening occurs at lower gadolinium concentrations, whereas T2 shortening occurs at higher gadolinium concentrations, which is of limited clinical use due to the increased risk of toxicity. Therefore, in conventional clinical practice T1 is evaluated after the administration of extracellular agents (7). Contrast agents containing transition metal ions, such as high spin manganese (II) and superparamagnetic iron oxide such as iron (III) oxides, affect the T2 relaxation strongly (8,9).

Gadolinium-based contrast agents: paramagnetic

Gadolinium (III)-based contrast agents are categorised into three groups: extracellular fluid (ECF) agents, blood pool contrast agents (BPCAs) and organ-specific agents.

Manganese-based contrast agents: paramagnetic

Manganese, in the form of manganese chelates or manganese-based nanoparticles, is used as a contrast agent. Manganese chelates, including manganese dipyridoxyl diphosphate (Mn-DPDP), markedly enhance the T1 signal intensity, and has been used to detect hepatic lesions. In the human body, the chelate dissociates into manganese and DPDP. Manganese is taken up by the liver cells and excreted into the bile, whereas the DPDP component is excreted by the kidneys (10). Research on Mn-based nanoparticles is not as detailed in comparison with other well-studied nanoparticles based on iron oxide (11).

Manganese-enhanced MRI (MEMRI) uses manganese ions (Mn2+) and this contrast agent has applications in animal experiments (12). Mn2+ enters cells through calcium (Ca2+) channels and thus, this group of contrast agents may be used for functional brain imaging (13). A previous MRI study has suggested that Mn2+ carbon nanostructure complexes of graphene oxide nanoplatelets and graphene oxide nanoribbons are highly effective MRI contrast agents (14).

Iron oxide contrast agents: superparamagnetic

There are two types of iron oxide contrast agents: superparamagnetic iron oxide (SPIO) and ultrasmall superparamagnetic iron oxide (USPIO). Superparamagnetic contrast agents consist of suspended colloids of iron oxide nanoparticles. When applied during imaging, they reduce the intensity of the T2 signals in the tissues which absorb the contrast agent. SPIO and USPIO have achieved successful outcomes in the diagnosis of liver tumors in some cases (15). Two decades ago, SPIO was the first nanoparticulate MRI contrast agent to be introduced as a liver contrast agent, and it is still used for clinical imaging (16). SPIOs and USPIOs such as Feridex I.V., Resovist, Sinerem and Clariscan have been approved for use in the past. However, these agents are currently unavailable apart from the oral iron oxide contrast agent, Lumirem/GastroMARK.

The nano-sized dimensions and the particle shapes of this group of contrast materials allow for different biodistribution and applications that are not observed with other contrast agents. At present, nanoparticulate iron oxide is a popular and unique nanoparticulate agent used in clinical practice. However, owing to the sophisticated modern technology of molecular and cellular imaging, which makes disease-specific biomarkers visible at microscopic and molecular levels, other nanoparticles have also obtained greater attention as potential MRI contrast agents. Due to the enormous improvement in nanotechnology, novel nanoparticulate MRI contrast agents have been developed with further improved contrast abilities as well as other functions (16).

Iron platinum contrast agents: superparamagnetic

Compared with iron oxide nanoparticles, superparamagnetic iron platinum particles (SIPPs) are thought to possess significantly improved T2 relaxation properties. SIPPs have been encapsulated with phospholipids to create multifunctional SIPP stealth immunomicelles in order to specifically target human prostate cancer cells (17). These contrast agents are still under investigation and have not yet been studied in humans, to the best of our knowledge. This study revealed that multifunctional SIPP micelles have been synthesized and conjugated to a monoclonal antibody against prostate-specific membrane antigen. In addition, the complex specifically targeted human prostate cancer cells in vitro, suggesting that SIPPs may have the potential to be tumor-specific in the future (17).

3. Chemical composition and the presence or absence of metal atoms

MRI contrast agents may be divided into two classifications as mentioned previously. The first group is comprised of paramagnetic compounds, which include lanthanides such as gadolinium. The second group is comprised of transition elements such as manganese and iron.

In order to reduce the toxicity of metal ions, the concept of chelation has been introduced. To prepare contrast agents based on metallic ions, the technique of chelated complex formation is widely used. The acute and the chronic toxic side-effects induced by the metal ion as well as the chelating agent are markedly reduced due to complexation (18).

As mentioned previously, gadolinium is used as a gadolinium (III) ion. Gadolinium (III) is weakly bound to serum proteins and may be displaced by ligands. Lanthanide salts generally hydrolyse into hydroxides, which are taken up by the reticuloendothelial system (RES) and accumulate in the body, particularly in the liver, spleen and bone, thereby causing potential toxicity. Lanthanide ions are excreted into both urine and faeces, unlike manganese ions which are almost exclusively excreted by GI elimination, via the biliary route. To overcome the aforementioned problems, these elements are administered in chelated forms.

Gadolinium-based chelation complexes

Fig. 2 illustrates the gadolinium-based chelation complexes used in clinical practice. There are three types of gadolinium (III)-based chelates (18).

Ionic and hydrophilic complexes

Ionic and hydrophilic complexes include gadolinium (III) diethylenetriamine pentaacetate (Gd-DTPA, also known as gadopentate dimeglumine), Gd(III) 1,4,7,10-tetrazacyclododecane NN′N″N‴-tetra-acetate (Gd-DOTA, gadoterate) (19) and Gd(III) polyaspartate.

Nonionic and hydrophilic complexes

Nonionic hydrophilic chelates of gadolinium (III) include Gd3-diethylenetriamine pentaacetate-bis(methylamide) (Gd-DTPA-BMA, also known as gadodiamide) and a macrocyclic chelate analog of Gd-DOTA, where an acetic acid function is replaced by a 2-propanol radical (Gd-HP-DO3A, also known as gadoteridol) (20).

Ionic and lipophilic complexes

The other group of gadolinium complexes includes the Gd benzyl-oxy-methyl derivative of diethyltriamine pentaacetate dimethylglucamine salt (Gd-BOPTA, also known as gadobenate dimeglumine) and Gd ethoxybenzyl diethylentriamine pentaacetate (Gd-EOB-DTPA, also known as gadoxetate) (18).

4. Route of administration

MRI contrast agents may be administered intravenously or orally. The route of administration is dependent on the subject of interest. A list of contrast agents is presented in Table I.

Table I

Agents administered orally.

Table I

Agents administered orally.

Short nameGeneric nameTrade nameEnhancement
Gd-DTPAaGadopentate dimeglumineMagnevist EnteralPositive
–aFerric amonium citrateFerriseltzPositive
–aManganese chlorideLumenHancePositive
–aGadolinium-loaded zeoliteGadolitePositive
OMPaFerristene (MPIO)AbdoscanNegative
AMI-121bFerumoxsil (MPIO)Lumirem/GastroMARKNegative
PFOBbPerfluoro-octylbromideImagent GINegative
Intravenous contrast agents

Intravenous MRI contrast agents are comprised of chelates of paramagnetic ions, both ionic and nonionic. The particulates are isolated in the liver, spleen and lymph nodes. The intravascular agents are confined to the blood pool and to specific tumors.

Ionic intravenous contrast agents

The first intravenous contrast agents to be used were the chelates of paramagnetic ions Cr and Gd in combination with ethylenediaminetetraacetic acid (EDTA). However, EDTA was relatively unstable, and was found to cause toxic effects in an animal study (21). Gd-DTPA has been successfully used due to its stability and reliability, and it is the first intravenous MRI contrast agent to be approved for use in humans (Magnevist; Berlex Laboratories). Gd has a large magnetic moment, exceeded only by dysprosium (III) and holmium (III). Even at low concentrations, it possesses strong paramagnetic properties and low toxicity. The paramagnetic properties are due to the fact that it has seven unpaired electrons, as stated previously. Following intravenous administration, it is distributed in the intravascular and extracellular fluid spaces, and then rapidly excreted into urine (22).

Nonionic intravenous contrast agents

Nonionic contrast agents have been developed in parallel with iodinated contrast materials. Some side effects are due to the fact that ionic chelates are hyperosmolar. In contrast to ionic agents, nonionic agents are relatively hypoosmolar. Gadodiamide (Omniscan; Winthrop Pharmaceuticals) is a nonionic complex, which has only two-fifths of the osmolality of Gd-DTPA. Owing to a median lethal dose of 34 mmol/kg, gadodiamide has a safety ratio 2–3 times that of Gd-DOTA, and 3–4 times that of Gd-DTPA. The administration of gadodiamide does not cause abnormalities in serum bilirubin levels. However, one study conducted in 73 individuals demonstrated that elevated serum iron levels are a potential concern with an incidence of 8.2%, and a similar efficacy to that of Gd-DTPA (23). Gadoteridol (ProHance; Squibb) is the third kind of intravenous contrast agent sold on the market. It is a nonionic contrast agent with osmolarity similar to that of gadodiamide (24).

MRI oral contrast agents (OCAs)

The oral administration of contrast agents is appropriate for GI tract scans. Naturally prepared fruit juices such as Medlar fruit juice, blueberry juice and green tea, have been studied as MRI contrast agents for several years. Artificial OCAs are based on the heavy metal ions such as gadolinium, manganese (III), manganese (II), copper (II) and iron (III). Air and clay are used to reduce the T2 signal intensity (25,26). Gadolinium-based agents, SPIO, manganese-containing agents and barium sulfate suspensions have been studied as oral MRI contrast agents (27,28). The oral administration of MRI contrast agents containing manganese is a novel, noninvasive method for imaging (27). Barium sulfate suspensions are useful as negative oral MRI contrast agents (28). Divalent manganese ions are paramagnetic and greatly reduce the T1 relaxation time, thereby increasing T1 signal intensity. Manganese enters excitable cells such as neurons and myocardiocytes through various calcium channels. Thus, manganese acts as an indicator of calcium channel activity (27). However, the intravascular route of administration of MRI contrast agents is more useful and is the more commonly used route for MRI scans.

5. Effect on the image

Paramagnetic contrast agents, apart from dysprosium-based compounds, are positive agents and they exert similar effects on T1 imaging and T2 imaging. However, as the T1 of tissues is much higher than the T2, the predominant effect at low does is that of T1 shortening (29). The tissues absorbing such agents become bright on T1-weighted images.

Negative contrast agents reduce T2 signals by shortening the T2 relaxation time. Superparamagnetic and ferromagnetic agents belong to this group. However, reducing the particle size of ferromagnetic particles size results in the permanent loss of magnetic properties, and a change to become superparamagnetic particles (30). These compounds may also become T1 agents, depending on particle size and coating.

6. Biodistribution and applications

Extracellular fluid (ECF) agents

ECF agents (so called intravenous contrast agents) are distributed within the extracellular space. These agents have been used for the longest period of time in liver imaging, and they remain the most commonly used and well-documented. ECF agents are comprised of gadolinium chelated to an organic compound such as DTPA (31–33). A list of the ECF agents is presented in Table II.

Table II

ECF space agents.

Table II

ECF space agents.

Short nameGeneric nameTrade nameEnhancement and physiochemical effects
Gd-DTPAaGadopentate dimeglumineMagnevist Positive-ionic-linear
Gd-DOTAaGadoterate meglumineDotarem, Artirem Positive-ionic-macrocyclic
Gd-DTPA-BMAaGadodiamide injectionOmniscan Positive-nonionic-linear
Gd-HP-DO3AaGadoteridol injectionProHance Positive-nonionic-macrocylic
Gd-DTPA-BMEAaGadoversetamideOptiMARK Positive-nonionic-linear
Gd-DO3A-butrolaGadobutrolGadovist Positive-nonionic-macrocyclic
Gd-BOPTAaGadobenate dimeglumineMultiHance Positive-ionic-linear

The pharmacokinetics of gadolinium chelates mimic that of iodinated contrast agents for computed tomography (CT). The contrast agents circulate and then freely distribute in the extracellular space. ECF agents are mainly eliminated by renal excretion. Gadolinium enters the liver through the hepatic artery and portal vein, and is freely redistributed into the interstitial space. In contrast to iodine molecules which are imaged by CT, the effect of gadolinium is assessed by MRI rather than the molecule itself. Gadolinium exhibits an amplification effect as a number of adjacent water protons are relaxed by a single gadolinium atom. As a result, MRI is more sensitive to the effects of gadolinium than CT is to the effects of iodine (29,33).


BPCAs, also known as intravascular contrast agents, remain in the intravascular space much longer and are excreted more slowly than their ECF counterparts, thus providing a longer time window for the imaging of blood vessels. These agents are currently under investigation for use in angiography, which may be performed in the equilibrium phase (34). A list of BPCAs is presented in Table III.

Table III


Table III


Short nameGeneric nameTrade nameEnhancement
NC-100150bPEG-feron (USPIO)ClariscanPositive
SH U 555 CbFerucarbotran (USPIO)SupravistPositive
MS-325aGadofosvesetAngioMARK, Vasovist, AblavarPositive
AMI-227cFerumoxtran-10 (USPIO)Sinerem/CombidexPositive or negative
Gd-BOPTAaGadobenate dimeglumineMultiHancePositive

The BPCAs may be classified into the following three categories based on their mechanism of action: i) systems based on the noncovalent binding of low-molecular Gd to human serum albumin (HSA) to prevent immediate leakage into the interstitial space (35,36); ii) systems incorporating polymers or liposomes based on an increase in the size of the contrast agent, which slows down leakage through endothelial pores (37,38); and iii) systems based on nanoparticles, involving a change in the route of elimination (39–41). These agents may be classified broadly into three categories: USPIO particles, agents that reversibly bind to plasma proteins, and macromolecules (42,43).

Targeted and organ-specific contrast agents

The primary aim of MRI contrast agent development is to identify agents which are capable of targeting specific tissues. A list of such compounds is presented in Table IV. It is important to consider the following three parameters in order to optimize the development of targeted and organ-specific contrast agents: i) improvement of the enhancing effect as high and ultrahigh fields call for a different contrast agent compared with low and medium high fields; ii) selective distribution in the body as it is necessary for these contrast agents to accumulate (organ-or pathology-specific tracers) at the required site in order to reach high local concentrations and iii) improvement of tolerance: although tolerance of existing compounds is already very good, this means, the compound has to be inert chemically and biologically, and also has to be completely eliminated from the body.

Table IV

Targeted/organ-specific agents.

Table IV

Targeted/organ-specific agents.

Short nameGeneric nameTrade nameEnhancement and physiochemical effects
Mn-DPDPcMangafodipir trisodiumTreslascanPositive/liver
Gd-EOB-DTPAaGadoxetatePrimovist, EovistPositive-ionic-linear/liver
Gd-BOPTAaGadobenate dimeglumineMultiHancePositive-ionic-linear/liver
AMI-25aFerumoxides (SPIO)Endorem, FeridexNegative/liver
SH U 555 AcFerucarbotran (SPIO)Resovist, CliavistNegative/liver
AMI-227cFerumoxtran-10 (USPIO)Sinerem, CombidexPositive or negative/lymph nodes
Gadofluorine-MbPositive/(lymph nodes, CNS)
Mn-DPDPbMangafodipir trisodiumPositive/myocardium
Dy-DTPA-BMAbSprodiamide injectionNegative/myocardial and brain perfusion
Gd-DTPA-mesoporphyrinbGadophrinPositive/myocardium, necrosis

Iron oxides and liposomes have attracted particular interest as potential organ-specific agents. Iron oxide particles are imported into the cells of the RES through phagocytosis, which provides selective access to the liver, spleen, lymph nodes, and bone marrow. These agents can either be positive (T1) or negative (T2/T2*) enhancers, depending on particle size, composition, concentration and saturation magnetization of the material as well as the equipment hardware and pulse sequences used. The biodistribution of iron oxides is determined by size, shape, charge, hydrophilicity, chemical composition and surface coating (44). The majority of compounds are polydisperse and polycrystalline. However, actively targeted iron oxides, which tend to contain smaller superparamagnetic labels, are monodisperse and monocrystalline. For intravenous use, iron oxide particles should be <50 nm in order to avoid entrapment in the lungs.

Another group of particulate contrast agents are liposomes. Paramagnetic ions may either be encapsulated in the aqueous compartment of the liposomes or be linked to their lipid bilayers. More sophisticated liposome compounds have been developed including phospholipid spin-labeled and amphipathic chelate complexes.

The primary organ selected for developing passive targeting compounds (vascular, hepatobiliary, and reticuloendothelial) is the liver. In addition to vascular structures, both hepatocytes and the RES may be targeted. By dynamic examinations, vascular structures as well as highly vascularized lesions are commonly highlighted with the conventional low molecular weight contrast agents. Both Gd-EOB-DTPA and Gd-BOPTA are positive gadolinium-based agents with lipophilic side groups. Gd-EOB-DTPA is a liver-specific agent whereas Gd-BOPTA is a multipurpose contrast agent, well suited for liver imaging (45). Mn-DPDP is a positive multipurpose agent, which taken up by hepatocytes (46). Contrast enhancement appears to be due to the limited release of the manganese ion and this effect is long lasting and may be achieved with doses as low as 10 mmol/kg body weight.

Further applications

Some contrast agents may also be capable of targeting other organs such as the spleen, pancreas, bone marrow, lymph nodes, adrenals, muscles and particularly the heart as well as inflammation and specific tumors. However, they are not yet ready for use in clinical practice.

7. Future prospects and conclusions

The first MRI contrast agent to be used was ferric chloride in 1981. Over the past 3 decades, many contrast agents have been developed for use in clinical practice and some of them were withdrawn as result of safety concerns. The MRI contrast agents discovered to date may be classified into various groups according to a number of criteria: chemical composition, the presence of metal atoms, route of administration, magnetic properties, effect on the image, biodistribution and further applications. As a result there are variations in the clinical implications, mechanisms of action, safety, pharmacokinetics and pharmacodynamics of these contrast agents. Currently, newer and safer MRI agents capable of targeting organs, sites of inflammation and specific tumors are under investigation in order to develop contrast agents with higher disease specificity.



magnetic resonance imaging








superparamagnetic iron oxide


ultrasmall superparamagnetic iron oxide


superparamagnetic iron platinum particle


manganese dipyridoxyl diphosphate or mangafodipir trisodium


manganese-enhanced MRI


gadolinium (III) diethylenetriamine pentaacetate


gadoterate dotarem


gadolinium ethoxybenzyl diethylenetriamine pentaacetate or gadoxetate




gadolinium 3-diethylenetriamine pentaacetate-bis(methylamide)




gadobenate dimeglumine


oral contrast agent




computed tomography


extracellular fluid


blood pool contrast agent


human serum albumin


reticuloendothelial system



Young IR, Clarke GJ, Bailes DR, Pennock JM, Doyle FH and Bydder GM: Enhancement of relaxation rate with paramagnetic contrast agents in NMR imaging. J Comput Tomogr. 5:543–547. 1981. View Article : Google Scholar : PubMed/NCBI


Carr DH, Brown J, Bydder GM, Weinmann HJ, Speck U, Thomas DJ and Young IR: Intravenous chelated gadolinium as a contrast agent in NMR imaging of cerebral tumours. Lancet. 1:484–486. 1984. View Article : Google Scholar : PubMed/NCBI


Tang JB, Sheng YQ, Hu HJ and Shen YQ: Macromolecular MRI contrast agents: structures, properties and applications. Prog Polym Sci. 38:462–502. 2013. View Article : Google Scholar


Geraldes CFGC and Laurent S: Classification and basic properties of contrast agents for magnetic resonance imaging. Contrast Media Mol Imaging. 4:1–23. 2009. View Article : Google Scholar : PubMed/NCBI


Mitchell DG: Liver I: Currently available gadolinium chelates. Magn Reson Imaging Clin N Am. 4:37–51. 1996.PubMed/NCBI


Wood ML and Hardy PA: Proton relaxation enhancement. J Magn Reson Imaging. 3:149–156. 1993. View Article : Google Scholar : PubMed/NCBI


Gandhi SN, Brown MA, Wong JG, Aguirre DA and Sirlin CB: MR contrast agents for liver imaging: what, when, how. Radiographics. 26:1621–1636. 2006. View Article : Google Scholar : PubMed/NCBI


Shokrollahi H: Contrast agents for MRI. Mater Sci Eng C. 33:4485–4497. 2013. View Article : Google Scholar


Yurt A and Kazanci N: Investigation of magnetic properties of various complexes prepared as contrast agents for MRI. J Mol Struct. 892:392–397. 2008. View Article : Google Scholar


Harisinghani MG, Jhaveri KS, Weissleder R, Schima W, Saini S, Hahn PF and Mueller PR: MRI contrast agents for evaluating focal hepatic lesions. Clin Radiol. 56:714–725. 2001. View Article : Google Scholar : PubMed/NCBI


Zhen ZP and Xie J: Development of manganese-based nanoparticles as contrast probes for magnetic resonance imaging. Theranostics. 2:45–54. 2012. View Article : Google Scholar : PubMed/NCBI


Silva AC, Lee JH, Aoki L and Koretsky AR: Manganese-enhanced magnetic resonance imaging (MEMRI): methodological and practical considerations. NMR Biomed. 17:532–543. 2004. View Article : Google Scholar : PubMed/NCBI


Lin YJ and Koretsky AP: Manganese ion enhances T-1-weighted MRI during brain activation: An approach to direct imaging of brain function. Magn Reson Med. 38:378–388. 1997. View Article : Google Scholar : PubMed/NCBI


Paratala BS, Jacobson BD, Kanakia S, Francis LD and Sitharaman B: Physicochemical characterization, and relaxometry studies of micro-graphite oxide, graphene nanoplatelets, and nanoribbons. PloS One. 7:e381852012. View Article : Google Scholar : PubMed/NCBI


Nakamura H, Ito N, Kotake F, Mizokami Y and Matsuoka T: Tumor-detecting capacity and clinical usefulness of SPIO-MRI in patients with hepatocellular carcinoma. J Gastroenterol. 35:849–855. 2000. View Article : Google Scholar : PubMed/NCBI


Na HB, Song IC and Hyeon T: Inorganic nanoparticles for MRI contrast agents. Adv Mater. 21:2133–2148. 2009. View Article : Google Scholar


Taylor RM, Huber DL, Monson TC, Ali AMS, Bisoffi M and Sillerud LO: Multifunctional iron platinum stealth immunomicelles: targeted detection of human prostate cancer cells using both fluorescence and magnetic resonance imaging. J Nanopart Res. 13:4717–4729. 2011. View Article : Google Scholar : PubMed/NCBI


Thunus L and Lejeune R: Overview of transition metal and lanthanide complexes as diagnostic tools. Coordin Chem Rev. 184:125–155. 1999. View Article : Google Scholar


Sijens PE, van den Bent MJ, Nowak PJ, van Dijk P and Oudkerk M: 1H chemical shift imaging reveals loss of brain tumor choline signal after administration of Gd-contrast. Magn Reson Med. 37:222–225. 1997. View Article : Google Scholar : PubMed/NCBI


Chang CA: Magnetic resonance imaging contrast agents. Design and physicochemical properties of gadodiamide. Invest Radiol. 28(Suppl 1): S21–S27. 1993. View Article : Google Scholar : PubMed/NCBI


Runge VM, Clanton JA, Herzer WA, Gibbs SJ, Price AC, Partain CL and James AE Jr: Intravascular contrast agents suitable for magnetic resonance imaging. Radiology. 153:171–176. 1984. View Article : Google Scholar : PubMed/NCBI


Runge VM, Schoerner W, Niendorf HP, Laniado M, Koehler D, Claussen C, Felix R and James AE Jr: Initial clinical evaluation of gadolinium DTPA for contrast-enhanced magnetic resonance imaging. Magn Reson Imaging. 3:27–35. 1985. View Article : Google Scholar : PubMed/NCBI


Kaplan GD, Aisen AM and Aravapalli SR: Preliminary clinical trial of gadodiamide injection: a new nonionic gadolinium contrast agent for MR imaging. J Magn Reson Imaging. 1:57–62. 1991. View Article : Google Scholar : PubMed/NCBI


Runge VM, Dean B, Lee C, Carolan F and Heard G: Phase III clinical evaluation of Gd-HP-DO3A in head and spine disease. J Magn Reson Imaging. 1:47–56. 1991. View Article : Google Scholar : PubMed/NCBI


Cordova-Fraga T, Sosa M, Hernandez-Gonzalez MA, Reyes-Aguilera JA, Solorio S, Ramirez C, Bautista-Flores E, Reynaga G, Avila-Rodriguez M and De la Roca-Chiapas JM: Medlar (Achras sapota L.) as oral contrast agent for MRI of the gastrointestinal tract. Appl Magn Reson. 42:161–167. 2012. View Article : Google Scholar


Mayo-Smith WW: Computed body tomography with MRI correlation. AJR Am J Roentgenol. 173:2661999.


Jacobs KE, Behera D, Rosenberg J, Gold G, Moseley M, Yeomans D and Biswal S: Oral manganese as an MRI contrast agent for the detection of nociceptive activity. NMR Biomed. 25:563–569. 2012. View Article : Google Scholar : PubMed/NCBI


Li KC, Tart RP, Fitzsimmons JR, Storm BL, Mao J and Rolfes RJ: Barium sulfate suspension as a negative oral MRI contrast agent: in vitro and human optimization studies. Magn Reson Imaging. 9:141–150. 1991. View Article : Google Scholar : PubMed/NCBI


Verloh N, Utpatel K, Haimerl M, Zeman F, Fellner C, Fichtner-Feigl S, Teufel A, Stroszczynski C, Evert M and Wiggermann P: Liver fibrosis and Gd-EOB-DTPA-enhanced MRI: A histopathologic correlation. Sci Rep. 5:154082015. View Article : Google Scholar : PubMed/NCBI


Weissleder R, Bogdanov A and Papisov M: Drug targeting in magnetic resonance imaging. Magn Reson Q. 8:55–63. 1992.PubMed/NCBI


Ahmad MW, Xu W, Kim SJ, Baeck JS, Chang Y, Bae JE, Chae KS, Park JA, Kim TJ and Lee GH: Potential dual imaging nanoparticle: Gd2O3 nanoparticle. Sci Rep. 5:85492015. View Article : Google Scholar : PubMed/NCBI


Edelman RR, Siegel JB, Singer A, Dupuis K and Longmaid HE: Dynamic MR imaging of the liver with Gd-DTPA: initial clinical results. AJR Am J Roentgenol. 153:1213–1219. 1989. View Article : Google Scholar : PubMed/NCBI


Balci NC and Semelka RC: Contrast agents for MR imaging of the liver. Radiol Clin North Am. 43:887–898. 2005. View Article : Google Scholar : PubMed/NCBI


Nolte-Ernsting C, Adam G, Bücker A, Berges S, Bjørnerud A and Günther RW: Abdominal MR angiography performed using blood pool contrast agents: comparison of a new superparamagnetic iron oxide nanoparticle and a linear gadolinium polymer. AJR Am J Roentgenol. 171:107–113. 1998. View Article : Google Scholar : PubMed/NCBI


Lauffer RB, Parmelee DJ, Dunham SU, Ouellet HS, Dolan RP, Witte S, McMurry TJ and Walovitch RC: MS-325: albumin-targeted contrast agent for MR angiography. Radiology. 207:529–538. 1998. View Article : Google Scholar : PubMed/NCBI


Cavagna FM, Lorusso V, Anelli PL, Maggioni F and de Haën C: Preclinical profile and clinical potential of gadocoletic acid trisodium salt (B22956/1), a new intravascular contrast medium for MRI. Acad Radiol. 9(Suppl 2): S491–S494. 2002. View Article : Google Scholar : PubMed/NCBI


Curtet C, Maton F, Havet T, Slinkin M, Mishra A, Chatal JF and Muller RN: Polylysine-Gd-DTPAn and polylysine-Gd-DOTAn coupled to anti-CEA F(ab′)2 fragments as potential immunocontrast agents. Relaxometry, biodistribution, and magnetic resonance imaging in nude mice grafted with human colorectal carcinoma. Invest Radiol. 33:752–761. 1998. View Article : Google Scholar : PubMed/NCBI


Løkling KE, Fossheim SL, Skurtveit R, Bjørnerud A and Klaveness J: pH-sensitive paramagnetic liposomes as MRI contrast agents: in vitro feasibility studies. Magn Reson Imaging. 19:731–738. 2001. View Article : Google Scholar : PubMed/NCBI


Jung CW and Jacobs P: Physical and chemical properties of superparamagnetic iron oxide MR contrast agents: ferumoxides, ferumoxtran, ferumoxsil. Magn Reson Imaging. 13:661–674. 1995. View Article : Google Scholar : PubMed/NCBI


Taupitz M, Schnorr J, Abramjuk C, Wagner S, Pilgrimm H, Hünigen H and Hamm B: New generation of monomer-stabilized very small superparamagnetic iron oxide particles (VSOP) as contrast medium for MR angiography: preclinical results in rats and rabbits. J Magn Reson Imaging. 12:905–911. 2000. View Article : Google Scholar : PubMed/NCBI


Jung KH, Kim HK, Park JA, Nam KS, Lee GH, Chang Y and Kim TJ: Gd Complexes of DO3A-(Biphenyl-2,2′-bisamides) Conjugates as MRI Blood-Pool Contrast Agents. ACS Med Chem Lett. 3:1003–1007. 2012. View Article : Google Scholar : PubMed/NCBI


Bourrinet P, Bengele HH, Bonnemain B, Dencausse A, Idee JM, Jacobs PM and Lewis JM: Preclinical safety and pharmacokinetic profile of ferumoxtran-10, an ultrasmall superparamagnetic iron oxide magnetic resonance contrast agent. Invest Radiol. 41:313–324. 2006. View Article : Google Scholar : PubMed/NCBI


Weinmann HJ, Ebert W, Misselwitz B and Schmitt-Willich H: Tissue-specific MR contrast agents. Eur J Radiol. 46:33–44. 2003. View Article : Google Scholar : PubMed/NCBI


Corot C and Warlin D: Superparamagnetic iron oxide nanoparticles for MRI: contrast media pharmaceutical company R&D perspective. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 5:411–422. 2013.PubMed/NCBI


Runge VM: A comparison of two MR hepatobiliary gadolinium chelates: Gd-BOPTA and Gd-EOB-DTPA. J Comput Assist Tomogr. 22:643–650. 1998. View Article : Google Scholar : PubMed/NCBI


Torres CG, Lundby B, Sterud AT, McGill S, Gordon PB and Bjerknes HS: MnDPDP for MR imaging of the liver - Results from the European phase III studies. Acta Radiol. 38:631–637. 1997.PubMed/NCBI

Sours: https://www.spandidos-publications.com/10.3892/ijmm.2016.2744

253 254 255 256 257