WO2014188368A1

WO2014188368A1 - A method for grey zone imaging using relative r1 changes

Info

Publication number: WO2014188368A1
Application number: PCT/IB2014/061620
Authority: WO
Inventors: Tobias Ratko Voigt; Andrea Jane WIETHOFF; Tobias Richard Schaeffter; Reza Razavi; Zhong Chen; Christopher Aldo RINALDI
Original assignee: Koninklijke Philips N.V.; Philips Deutschland Gmbh; King's College London
Priority date: 2013-05-22
Filing date: 2014-05-22
Publication date: 2014-11-27

Abstract

A medical system (10) and method (100) characterizes myocardium. A pre-contrast magnetic resonance (MR) data set and a post-contrast MR data set of the myocardium are generated using a cardiac T1 mapping sequence. The pre-and post-contrast MR data sets are reconstructed to pre-and post-contrast T1 maps, respectively. A map of the relative change in an R1 relaxivity of a contrast agent is calculated from the pre-and post- contrast T1 maps. The contrast agent is used to generate the pre- and post-contrast MR data sets, and the R1 relaxivity is the inverse of T1. Values of the map of relative change in the R1 relaxivity are compared to cutoffs to discriminate between grey zone and another type of myocardium. Grey zone is a mixture of normal and fibrotic myocardium.

Description

A Method For Grey Zone Imaging Using Relative Rl Changes

The present application relates generally to magnetic resonance (MR) imaging. It finds particular application in conjunction with grey zone imaging, and will be described with particular reference thereto. However, it is to be understood that it also finds application in other usage scenarios and is not necessarily limited to the aforementioned application.

In myocardial infarction, the area at risk is defined as the region that comprises scarred and/or fibrotic tissue (i.e., the scar core) and the border zone where there is an admixture of fibrotic and surviving myocytes (i.e., the grey zone). Late gadolinium enhanced (LGE) inversion-recovery imaging in cardiac magnetic resonance (CMR) imaging is the standard approach to visualize regional myocardial fibrosis. Using LGE, it is possible to perform a more detailed characterization of the degree of scarring in the infarct border zone. This area is referred to as the grey zone, because of the intermediate signal intensity (SI) between the hyper-enhanced scar core and the nulled, noninfarct myocardium seen on LGE images.

Studies based on semi-quantitative Si-based scar segmentation methods using

LGE have demonstrated that the grey zone is an independent prognostic indicator of mortality and ventricular arrhythmia in post-infarct patients. See, for example, one or more of: 1) Andrew T. Yan et al. Characterization of the peri-infarct zone by contrast-enhanced cardiac magnetic resonance is a powerful predictor of post-myocardial infarction mortality. Circulation 2006; 114:32-39; 2) Andre Schmidt et al. Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia susceptibility in patients with left ventricular dysfunction. Circulation 2007; 115:2006-2014; and 3) Stijntje D. Roes et al. Infarct tissue heterogeneity assessed with contrast-enhanced MRI predicts spontaneous ventricular arrhythmia in patients with ischemic cardiomyopathy and implantable cardioverter-defibrillator. Circulation: Cardiovascular Imaging. 2009; 2: 183-190.

Despite the wide adoption of standard LGE-CMR imaging for regional myocardial fibrosis assessment, it is not without limitations, such as inaccurate inversion time selection and therefore suppression of diffuse fibrosis. The present application provides a new and improved system and method which overcome these problems and others. In accordance with one aspect, a medical system for characterizing myocardium is provided. The medical system includes at least one processor programmed to generate a pre-contrast magnetic resonance (MR) data set and a post-contrast MR data set of the myocardium using a cardiac Tl mapping sequence. The at least one processor is further programmed to reconstruct the pre- and post-contrast MR data sets to pre- and post-contrast Tl maps, respectively. Even more, the at least one processor is programmed to calculate a map of the relative change in an Rl relaxivity due to administration of a contrast agent from the pre- and post-contrast Tl maps. The contrast agent is used to alter Rl between the pre- and post-contrast MR data sets, and the Rl relaxivity is the inverse of Tl. Moreover, values of the map of relative change in the Rl relaxivity are compared to cutoffs to discriminate between grey zone and another type of myocardium. Grey zone is a mixture of normal and fibrotic myocardium.

In accordance with another aspect, a medical method for characterizing myocardium is provided. A pre-contrast magnetic resonance (MR) data set and a post- contrast MR data set of the myocardium are generated using a cardiac Tl mapping sequence. The pre- and post-contrast MR data sets are reconstructed to pre- and post-contrast Tl maps, respectively. A map of the relative change in an Rl relaxivity due to administration of a contrast agent is calculated from the pre- and post-contrast Tl maps. The contrast agent is used to alter Rl relaxivity between the pre- and post-contrast MR data sets, and the Rl relaxivity is the inverse of Tl. Values of the map of relative change in the Rl relaxivity are compared to cutoffs to discriminate between grey zone and another type of myocardium. Grey zone is a mixture of normal and fibrotic myocardium.

In accordance with another aspect, a medical system for characterizing myocardium is provided. The medical system includes a magnetic resonance (MR) scanner generating MR data sets from MR signals received from an imaging volume. The medical system further includes a myocardium module configured to generate a pre-contrast MR data set and a post-contrast MR data set of the myocardium using a cardiac Tl mapping sequence carried out using the MR scanner. The myocardium module is further configured to calculate a map of the relative change in an Rl relaxivity due to administration of a contrast agent from the pre- and post-contrast MR data sets. The contrast agent is used to alter Rl relaxivity between the pre- and post-contrast MR data sets, and the Rl relaxivity is the inverse of Tl. The myocardium module is further configured to compare values of the map of relative change in the Rl relaxivity to cutoffs to discriminate between grey zone and another type of myocardium. Grey zone is a mixture of normal and fibrotic myocardium.

One advantage resides in improved grey zone imaging.

Another advantage resides in removal of the influence of imprecise inversion time selection.

Another advantage resides in true quantitative signal quantification on a standardized scale.

Still further advantages of the present invention will be appreciated to those of ordinary skill in the art upon reading and understand the following detailed description.

The invention may take form in various components and arrangements of components, and in various steps and arrangements of steps. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.

FIGURE 1 illustrates a magnetic resonance (MR) system carrying out an enhanced method for characterizing myocardium.

FIGURE 2 illustrates an enhanced method for characterizing myocardium.

FIGURE 3A illustrates receiver operating characteristic (ROC) curves for pre- contrast Rl, post-contrast Rl, and relative change in relaxivity, of remote healthy myocardium.

FIGURE 3B illustrates ROC curves for pre-contrast Rl, post-contrast Rl, and relative change in relaxivity, of scar core.

FIGURE 3C illustrates ROC curves for pre-contrast Rl, post-contrast Rl, and relative change in relaxivity, of grey zone.

Mean Rl values for remote healthy myocardium, grey zone and scar core are significantly different on the pre- and the post-contrast Rl maps. An Rl value is the inverse of a Tl value. The present application proposes to use relative Rl change A¾ = (¾sr* - Rvss&) ^Si.prz to distinguish between remote healthy myocardium, grey zone and scar core. Receiver operating curve (ROC) analysis showed that using relative Rl change provides excellent distinction between remote healthy myocardium, grey zone and scar core in comparison to either pre- or post-contrast Rl alone.

The present application proposes an enhanced method of characterizing the myocardium that involves assessing the tissue longitudinal relaxation time using a Tl mapping. This technique eliminates the influence of imprecise inversion time selection during image acquisition and allows true quantitative signal quantification on a standardized scale of each image voxel to characterize tissue heterogeneity. Further, the technique was shown to work with patients with a history of myocardial infarction.

According to a preferred embodiment, a cardiac magnetic resonance (CMR) Tl mapping is generated for both pre- and post-contrast agent administration. The Tl mappings are optionally corrected for motion and/or heart rate and other influences that could degrade image quality. A map of relative change in relaxivity (ARl) is then calculated from the Tl mappings, optionally as corrected for motion and/or heart rate. The relative change in relaxivity values are compared to cut-off ARl values to diagnose the patient and distinguish between remote healthy myocardium, grey zone and scar core. The cut-off ARl values can be determined using receiver operating curve (ROC) analysis.

With reference to FIGURE 1, a magnetic resonance (MR) imaging system 10 utilizes MR to image a region of interest (ROI) of a patient 12. The system 10 includes a scanner 14 defining an imaging volume 16 (indicated in phantom) sized to accommodate the ROI. A patient support can be employed to support the patient 12 in the scanner 14 and facilitates positioning the ROI in the imaging volume 16.

The scanner 14 includes a main magnet 18 that creates a strong, static Bo magnetic field extending through the imaging volume 16. The main magnet 18 typically employs superconducting coils to create the static Bo magnetic field. However, the main magnet 18 can also employ permanent or resistive magnets. Insofar as superconducting coils are employed, the main magnet 18 includes a cooling system, such as a liquid helium cooled cryostat, for the superconducting coils. The strength of the static Bo magnetic field is commonly one of 0.23 Tesla, 0.5 Tesla, 1.5 Tesla, 3 Tesla, 7 Tesla, and so on in the imaging volume 16, but other strengths are contemplated.

A gradient controller 20 of the scanner 14 is controlled to superimpose magnetic field gradients, such as x, y and z gradients, on the static Bo magnetic field in the imaging volume 16 using a plurality of magnetic field gradient coils 22 of the scanner 14. The magnetic field gradients spatially encode magnetic spins within the imaging volume 16. Typically, the plurality of magnetic field gradient coils 22 include three separate magnetic field gradient coils spatially encoding in three orthogonal spatial directions.

Further, one or more transmitters 24, such as a transceiver, are controlled to transmit Bi resonance excitation and manipulation radiofrequency (RF) pulses into the imaging volume 16 with one or more transmit coil arrays, such as a whole body coil 26 and/or a surface coil 28, of the scanner 14. The Bi pulses are typically of short duration and, when taken together with the magnetic field gradients, achieve a selected manipulation of magnetic resonance. For example, the Bi pulses excite the hydrogen dipoles to resonance and the magnetic field gradients encode spatial information in the frequency and phase of the resonance signal. By adjusting the RF frequencies, resonance can be excited in other dipoles, such as phosphorous, which tend to concentrate in known tissues, such as bones.

One or more receivers 30, such as a transceiver, are controlled to receive spatially encoded magnetic resonance signals from the imaging volume 16 and demodulate the received spatially encoded magnetic resonance signals to MR data sets. The MR data sets include, for example, k-space data trajectories. To receive the spatially encoded magnetic resonance signals, the receivers 30 use one or more receive coil arrays, such as the whole body coil 26 and/or the surface coil 28, of the scanner 14. The receivers 30 typically store the MR data sets in a buffer memory.

A backend system 58 of the system 10 images the ROI using the scanner 14. The backend system 58 is typically remote from the scanner 14 and includes a plurality of modules 60, discussed hereafter, to perform the imaging of the ROI using the scanner 14. Advantageously, the backend system can characterize myocardium without the in the influence of imprecise inversion time selection and provide true quantitative signal quantification on a standardized scale.

A control module 62 of the backend system 58 controls overall operation of the backend system 58. The control module 62 suitably displays a graphical user interface (GUI) to a user of the backend system 58 using a display device 64 of the backend system 58. Further, the control module 62 suitably allows the operator to interact with the GUI using a user input device 66 of the backend system 58. For example, the user can interact with the GUI to instruct the backend system 58 to coordinate the imaging of the ROI.

A data acquisition module 68 of the backend system 58 performs MR scans of the ROI. For each MR scan, the data acquisition module 68 controls the transmitters 24 and/or the gradient controller 20 according to scan parameters, such as number of slices, to implement an imaging sequence within the imaging volume 16. An imaging sequence defines a sequence of Bi pulses and/or magnetic field gradients that produce spatially encoded MR signals from the imaging volume 16. Further, the data acquisition module 68 controls the receivers 30, and the tune/detune control signal of the driver circuit 36, according to scan parameters to acquire spatially encoded MR signals to an MR data set. The MR data set is typically stored in at least one storage memory 70 of the backend system 58.

In preparing for MR acquisition, the ROI is positioned within the imaging volume 16. For example, the patient 12 is positioned on the patient support. The surface coil 28 is then positioned on the patient 12 and the patient support moves the ROI into the imaging volume 16.

A reconstruction module 72 of the backend system 58 reconstructs the MR data sets of the MR diagnostic scans into MR images or maps of the ROI. This includes, for each MR signal captured by the MR data sets, spatially decoding the spatial encoding by the magnetic field gradients to ascertain a property of the MR signal from each spatial region, such as a pixel or voxel. The intensity or magnitude of the MR signal is commonly ascertained, but other properties related to phase, relaxation time, magnetization transfer, and the like can also be ascertained. The MR images or maps are typically stored in the storage memory 70.

A myocardium module 74 of the backend system 58 carries out an enhanced method 100 of characterizing myocardium, shown in FIGURE 2. The method 100 eliminates the influence of imprecise inversion time selection during image acquisition and allows true quantitative signal quantification on a standardized scale of each image voxel to characterize tissue heterogeneity. Further, the method 100 was shown to work with patients with a history of myocardial infarction.

According to the method 100, the data acquisition module 68 is used to carry out a cardiac Tl mapping sequence to generate 102 a first MR data set before administration of a contrast agent (i.e., a pre-contrast MR data set) and a second MR data set a predetermined amount of time, such as 10 minutes, after administration of the contrast agent (i.e., a post-contrast MR data set). The cardiac Tl mapping sequence can be the modified look locker inversion recovery (MOLLI) sequence or any other accurate cardiac Tl mapping sequence. For more discussion regarding MOLLI, attention is directed to Daniel R. Messroghli et al. Modified Look- Locker inversion recovery (MOLLI) for high-resolution Tl mapping of the heart. Magnetic Resonance in Medicine 2004; 52: 141-6.

The pre- and post-contrast MR data sets can optionally undergo motion correction 104. Each of the pre- and post-contrast MR data sets typically includes a plurality of data subsets corresponding to images and/or maps used for Tl map reconstruction. For example, when the MOLLI sequence is employed, each of the pre- and post-contrast MR data sets includes 11 data subsets corresponding images and/or maps. A hierarchical adaptive local affine registration (HALAR) technique can be employed for motion correction across these data subsets. For more discussion regarding HALAR, attention is directed to Christian Buerger et al. Hierarchical adaptive local affine registration for fast and robust respiratory motion estimation. Medical Image Analysis 2011 ; 15: 551-564. Any other accurate motion correction technique could alternatively be used.

The reconstruction module 72 is used to reconstruct 106 the pre- and post- contrast MR data sets, optionally as corrected for motion, into pre- and post-contrast Tl maps, respectively. As discussed above, reconstruction includes spatially decoding MR signals to ascertain a property of the MR signal from each spatial region, such as a pixel or voxel.

The pre- and post-contrast Tl maps can optionally undergo heart rate (HR) correction 108 of Tl values. The HR correction can be performed by determining a correlation between HR and Tl and then using that correlation to normalize Tl values of the pre- and post-contrast Tl maps to the average HR while determining the correlation. For more discussion regarding this approach to HR correction, attention is directed to Daniel R. Messroghli et al. Human Myocardium: Single-Breath-hold MR Tl Mapping with High Spatial Resolution— Reproducibility Studyl . Radiology 2006; 238: 1004-1012. Other approaches to HR correction, before or after the reconstruction 106 are also contemplated.

The pre- and post-contrast Tl maps, optionally as corrected for HR, are used to calculate 110 a map of relative change in relaxivity (ARl). Tl longitudinal relaxation times can be expressed as Rl values: Rl = 1/Tl . Further, relative change in relaxivity can be expressed according to Equation 1, shown below.

AR^ — {R^_pre -cotitrast ~ ^l ast- contrast ) i ^i^tre-cfmtrast ( 1)

The pre-contrast Rl values (i.e.,

correspond to Rl values determined from the pre-contrast Tl map, and post-contrast Rl values (i.e., S^^^_g^^_f ) correspond to Rl values determined from the post-contrast Tl map. When determining the ARl map, the pre- and post-contrast Tl maps are registered to a common coordinate frame, if not already done, using a registration routine. Thereafter, ARl values are calculated for each pixel location of the the ARl map according to Equation 1. The ARl map can be calculated on a per pixel basis or on a per voxel basis.

Regarding the generation of the ARl map on a per pixel basis, the pixel locations of the ARl map consist of pixel locations common to both the pre- and post- contrast Tl maps. The ARl value of each of these common pixel locations is the difference between the Rl values at the common pixel location in the pre- and post-contrast Tl maps (i.e., ^p -co^srasr ^— -c n rast ) divided by the Rl value at the common pixel location in the pre-contrast Tl map.

Regarding the generation of the ARl map on a per voxel basis, the pixel locations of the ARl map consist of one or more of: 1) pixel locations found in the pre- and/or post-contrast Tl maps; and 2) pixel locations common to both the pre- and post- contrast Tl maps. The ARl value of each of the pixel locations of the ARl map is the ARl value for the corresponding voxel of the pixel location. The ARl value for each voxel can be a statistic (e.g., the average, mean, medium, maximum, minimum, etc.) of the ARl value of the pixel locations common to both the pre- and post-contrast Tl maps of the voxel. Alternatively, the ARl value for each voxel can be the ARl value determined according to Equation 1 from pre- and post-contrast statistics (e.g., the average, mean, medium, maximum, minimum, etc.) of the Tl or Rl values of the voxel in the pre- and post-contrast Tl maps, respectively. For example, the mean Rl values in each voxel can be used to determine the ARl value for the voxel.

The relationship between the pre- and post-contrast Rl values, the contrast agent concentration C and its relaxivity ¾ , a constant, is expressed by the function:

R = R 4- i" (2) In view of this, the proposed quantity is linearly proportional to the contrast agent concentration and its relaxivity constant and inversely proportional to pre-contrast Rl.

Δ , = i R R = L · r, /R i.prs

Thus, any difference in pre-contrast Rl values between different ROIs would be taken into account and any changes in post-contrast Rl values would potentially be magnified allowing greater separation of ARl between different ROIs. Prior art previously used the parameter Δϋ, =

to determine contrast agent concentrations. For further detail regarding this prior art approach to determining contrast agent concentrations, attention is directed to Bram F. Coolen et al. Regional Contrast Agent Quantification in a Mouse Model of Myocardial Infarction Using 3D Cardiac Tl Mapping. Journal of Cardiovascular Magnetic Resonance 2011; 13: 56.

The ARl map is compared 112 to cutoff ARl values to discriminate between remote healthy myocardium, scare core and grey zone (i.e., a mixture of normal and scarred myocardium), which can be used for diagnosis of a patient. As noted above, the grey zone is an independent prognostic indicator of mortality and ventricular arrhythmia in post-infarct patients. See, for example, Andrew T. Yan et al. Characterization of the peri-infarct zone by contrast-enhanced cardiac magnetic resonance is a powerful predictor of post-myocardial infarction mortality. Circulation 2006; 114:32-39

The cutoff values can be determined using receiver operating characteristic (ROC) analysis on training data. Namely, ROC curves for ARl can be generated for remote healthy myocardium, scar core and grey zone (i.e., a mixture of healthy and scar myocardium) using the training data. The horizontal and vertical axes correspond to specificity and sensitivity, respectively. The cutoffs for each of the three classes are determined as the ARl value located the shortest distance from to a sensitivity and specificity of (1, 1).

With reference to FIGURES 3A-C, graphs of ROC curves for remote healthy myocardium, scar core and grey zone, respectively, are provided. Each graph includes ROC curves for pre-contrast Rl, post-contrast Rl, and relative change in relaxivity ARl, as well as a reference line. Using these graphs, a ARl value between 1.78 and 2.68 provides the best prediction for the grey zone with a sensitivity of 0.75 and specificity of 0.94.

Each of the plurality of modules 60 can be embodied by processor executable instructions, circuitry (i.e., processor independent), or a combination of the two. The processor executable instructions are stored on at least one program memory 76 of the backend system 58 and executed by at least one processor 78 of the backend system 58. As illustrated, the plurality of modules 60 are embodied by processor executable instructions. However, as is to be appreciated, variations are contemplated. For example, the data acquisition module 68 can be circuitry.

As used herein, a memory includes one or more of: a non-transient computer readable medium; a magnetic disk or other magnetic storage medium; an optical disk or other optical storage medium; a random access memory (RAM), read-only memory (ROM), or other electronic memory device or chip or set of operatively interconnected chips; an Internet/Intranet server from which the stored instructions may be retrieved via the Internet/Intranet or a local area network; and the like. Further, as used herein, a processor includes one or more of a microprocessor, a microcontroller, a graphic processing unit (GPU), an application- specific integrated circuit (ASIC), an FPGA, and the like; a controller includes: (1) a processor and a memory, the processor executing computer executable instructions on the memory embodying the functionality of the controller; or (2) analog and/or digital hardware carrying out the functionality of the controller; a user input device includes one or more of a mouse, a keyboard, a touch screen display, a button, a switch, a voice recognition engine, and the like; a database includes one or more memories; a user output device includes a display device, a auditory device, and the like; and a display device includes one or more of a liquid crystal display (LCD) display, a light emitting diode (LED) display, a plasma display, a projection display, a touch screen display, and the like.

The invention has been described with reference to the preferred embodiments. Modifications and alterations may occur to others upon reading and understanding the preceding detailed description. It is intended that the invention be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

CLAIMS:

1. A medical system (10) for characterizing myocardium, said medical system (10) comprising:

at least one processor (78) programmed to:

generate a pre-contrast magnetic resonance (MR) data set and a post- contrast MR data set of the myocardium using a cardiac Tl mapping sequence;

reconstruct the pre- and post-contrast MR data sets to pre- and post- contrast Tl maps, respectively;

calculate a map of the relative change in an Rl relaxivity due to administration of a contrast agent from the pre- and post-contrast Tl maps, the contrast agent used to alter relaxivity between the pre- and post-contrast MR data sets, the Rl relaxivity being the inverse of Tl; and

compare values of the map of relative change in the Rl relaxivity to cutoffs to discriminate between grey zone and another type of myocardium, grey zone being a mixture of normal and fibrotic myocardium.

2. The medical system (10) according to claim 1, wherein the pre-contrast MR data set is generated before administering the contrast agent to the myocardium and the post- contrast MR data set is generated after administering the contrast agent to the myocardium.

3. The medical system (10) according to either one of claims 1 and 2, wherein the cardiac Tl mapping sequence is a modified look locker inversion recovery (MOLLI) sequence.

4. The medical system (10) according to any one of claims 1-3, wherein that at least one processor (78) is further programmed to:

motion correct the pre- and post-contrast MR data sets, the pre- and post-contrast Tl maps reconstructed from the motion corrected pre- and post-contrast MR data sets, respectively.

5. The medical system (10) according to claim 4, wherein the motion corrected pre- and post-contrast MR data sets are motion corrected using a hierarchical adaptive local affine registration (HALAR) technique.

6. The medical system (10) according to any one of claims 1-5, wherein the at least one processor (78) is further programmed to:

heart rate (HR) correct the pre- and post-contrast Tl maps, the HR corrected pre- and post-contrast Tl maps used to discriminate between grey zone and another type of myocardium. 7. The medical system (10) according to any one of claims 1-6, wherein the map of the relative change in the Rl relaxivity is calculated according to the following equation:

wherein Λ ₁ is the relative change in the Rl relaxivity, -contrast is /Tx rs-so erast' Ri&vst-oc crvsc ^{is i} /^Ti. ~:

^{is a va}l^{ue of} the pre-contrast Tl map, and Ti_jp-sst-centrast ^{s a} value of the post-contrast Tl map which corresponds to

¹ i TS -contrast · 8. The medical system (10) according to any one of claims 1-7, wherein the cutoffs are determined from a receiver operating characteristic (ROC) analysis.

9. The medical system (10) according to claim 8, wherein the at least one processor (78) is further programmed to:

generate ROC curves of relative change in the Rl relaxivity for remote healthy myocardium, scare core and grey zone; and

determine the cutoffs through analysis of the ROC curves.

10. A medical method (100) for characterizing myocardium, said medical method (100) comprising:

generating a pre-contrast magnetic resonance (MR) data set and a post-contrast MR data set of the myocardium using a cardiac Tl mapping sequence;

reconstructing the pre- and post-contrast MR data sets to pre- and post-contrast Tl maps, respectively; calculating a map of the relative change in an Rl relaxivity due to administration of a contrast agent from the pre- and post-contrast Tl maps, the contrast agent used to alter relaxivity between the pre- and post-contrast MR data sets, the Rl relaxivity being the inverse of Tl; and

comparing values of the map of relative change in the Rl relaxivity to cutoffs to discriminate between grey zone and another type of myocardium, grey zone being a mixture of normal and fibrotic myocardium.

11. The medical method (100) according to claim 10, wherein the pre-contrast MR data set is generated before administering the contrast agent to the myocardium and the post- contrast MR data set is generated after administering the contrast agent to the myocardium.

12. The medical method (100) according to either one of claims 10 and 11, wherein the cardiac Tl mapping sequence is a modified look locker inversion recovery (MOLLI) sequence.

13. The medical method (100) according to any one of claims 10-12, wherein that at least one processor (78) is further programmed to:

14. The medical method (100) according to claim 13, wherein the motion corrected pre- and post-contrast MR data sets are motion corrected using a hierarchical adaptive local affine registration (HALAR) technique.

15. The medical method (100) according to any one of claims 10-14, wherein the at least one processor (78) is further programmed to:

heart rate (HR) correct the pre- and post-contrast Tl maps, the HR corrected pre- and post-contrast Tl maps used to discriminate between grey zone and another type of myocardium.

16. The medical method (100) according to any one of claims 10-15, wherein the map of the relative change in the Rl relaxivity is calculated according to the following equation:

wherein

Ki ost-ccKtrast ^is !/Ti&ost-contrast, "^i^-contrast ^{is a} value of the pre-contrast Tl map, and _t__c._snSrasS is a value of the post-contrast Tl map which corresponds to

T

17. The medical method (100) according to any one of claims 10-16, wherein the cutoffs are determined from a receiver operating characteristic (ROC) analysis.

18. At least one processors (78) programmed to perform the method (100) according to any one of claims 10-17.

19. A non-transitory computer readable medium (76) carrying software which controls one or more processors (78) to perform the method (100) according to any one of claims 10-17.

20. A medical system (10) for characterizing myocardium, said medical system (10) comprising:

a magnetic resonance (MR) scanner (14) generating MR data sets from MR signals received from an imaging volume (16); and

a myocardium module (74) configured to:

generate a pre-contrast MR data set and a post-contrast MR data set of the myocardium using a cardiac Tl mapping sequence carried out using the MR scanner (14);

calculate a map of the relative change in an Rl relaxivity due to administration of a contrast agent from the pre- and post-contrast MR data sets, the contrast agent used to alter relaxivity between the pre- and post- contrast MR data sets, and the Rl relaxivity being the inverse of Tl; and compare values of the map of relative change in the Rl relaxivity to cutoffs to discriminate between grey zone and another type of myocardium, grey zone being a mixture of normal and fibrotic myocardium.