DEVICE FOR IMAGING A PATIENT'S HEART AND ASSOCIATED SYSTEM

Abstract

Title: DEVICE FOR IMAGING A PATIENT'S HEART AND ASSOCIATED SYSTEM Device for imaging a patient's heart, the heart comprising a myocardium, comprising at least one input interface, at least one processing unit and at least one output interface, the at least one processing unit being configured to implement by computer a processing step comprising: generation of a first black blood image from first black blood signals, generation of a first white blood image from first white blood signals, generation of a second image of a type taken between white blood and black blood from second signals of said type, determination, from said first black blood image, first white blood image and second image of said type, of first location data of a myocardial lesion and second location data of adipose tissue. Figure for abstract: Fig. 2

Description

Title of the invention: DEVICE FOR IMAGING A PATIENT'S HEART AND ASSOCIATED SYSTEM Field of invention

[0001] The invention relates to the field of cardiac magnetic resonance imaging (MRI) by late gadolinium enhancement or LGE, with reference to the English expression “Late Gadolinium Enhancement”.

[0002] The field of application of the invention relates more particularly to methods and systems for lesion characterization of the heart. This characterization makes it possible, in particular, to guide ablations.

[0003] The reference technique for the characterization of regional lesions including myocardial fibrosis is late gadolinium enhancement imaging in white blood or BR-LGE (for the English expression "bright-blood LGE") by inversion recovery such as the PSIR sequence (from the English expression "phase-sensitive inversion-recovery"). In this type of imaging, the cancellation of the viable myocardial signal is caused using inversion-recovery pulses, which makes it possible to visualize lesions with a high contrast between the healthy myocardial tissue and the lesions. However, for myocardial lesions adjacent to the blood chambers of the heart (right and left ventricles), the high intensity of the signal from the blood and therefore the low contrast between the lesions and the blood prevents the automatic, precise, reliable and robust characterization of scars, in particular sub-endocardial scars.

[0004] In order to circumvent this problem, dark blood LGE (BL-LGE) imaging techniques have been proposed. They allow simultaneous cancellation of the signals from healthy myocardium and blood, thus providing high contrast both between lesions and between blood and between lesions and healthy myocardium. By healthy myocardium is meant the largest representative area of the myocardium.

[0005] However, dark blood imaging techniques do not allow lesions to be correctly characterized, in particular to locate them precisely in relation to the myocardium, the contrast between the blood and the healthy myocardium not being sufficiently high.

[0006] The inventors of the present application have previously proposed in patent application FR2203782 a method for acquiring and merging black blood and white blood images making it possible to better locate heart scars while maintaining a short acquisition time.

[0007] In this type of image, however, scars are difficult to distinguish from tissues such as fatty tissue. It is then difficult to discriminate scars from such

[0008]

[0009]

[0010] fabrics. One aim of the invention is to improve the situation. To this end, the invention relates to a device for imaging the heart of a patient, the heart comprising a myocardium delimiting a cavity of the heart, comprising: • At least one input interface configured for: • receive, during a pair of inter-beats comprising two consecutive inter-beats, first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at a first echo duration, • receive, during the inter-beat pair, the first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration, • receive, during the inter-beat pair, second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration, • At least one processing unit configured to implement by computer a processing step comprising: • generation of a first black blood image from the first black blood signals, • generation of a first white blood image from the first white blood signals, • generation of a second image of said type from the second signals of said type, • determination, from the first black blood image, the second image of said type and possibly the first white blood image, of first location data of a myocardial lesion and second location data of adipose tissue, • At least one output interface configured to return the first location data of a myocardial lesion and the second location data of adipose tissue. Myocardial lesion and adipose tissue are understood to mean two areas of interest representing tissue singularities near or within a reference area represented by the myocardium. In these areas of interest, gadolinium accumulates preferentially because these areas are made up of interstitial tissue. Thus, these areas of interest are characterized, in the first dark blood image, by a pixel or voxel intensity greater than a predetermined threshold. The use of the second image of said type in combination with the first dark blood image (or al alternatively the first image in white blood) makes it possible to highlight the fatty tissues and therefore, to differentiate the two areas of interest previously mentioned.

[0011] In the present application, the term "lesion" of the myocardium is understood to mean a change in local tissue structure with respect to a tissue structure most representative of the myocardium. A distinction is made between pathological lesions and benign lesions. For example, certain lesions may modify the circulation of a nerve impulse in the myocardium. These lesions are referred to here as pathological. In another example, certain lesions may not modify the circulation of a nerve impulse. These lesions are referred to here as benign.

[0012] Thus, the device according to the invention makes it possible to determine and return, from three signals during a pair of consecutive inter-beats, two signals being of the same type among white blood and black blood acquired at two different echo durations, a combination of information relating to the existence and position of lesions of the myocardium and cardiac adipose tissues. Thus, the device according to the invention makes it possible to implement more precise clinical observations on the basis of a limited quantity of information.

[0013] Advantageously, the at least one processing unit is configured to, during the processing step: • Generate a first fused image from a first reference white blood image derived from the first white blood image and the first myocardial lesion location data.

[0014] Advantageously: • The at least one input interface is configured to receive, for a number N greater than 1 of inter-beat pairs, a plurality of N first black blood signals by black blood magnetic resonance and a plurality of N first white blood signals by white blood magnetic resonance, • The at least one processing unit is configured to generate N first black blood images and N first white blood images, • the first reference white blood image is a combination of the first N white blood images.

[0015] Thus, the quality of the first reference white blood image and the first reference black blood image is better, with for example a better signal to noise ratio, and the quality of the first merged image is improved.

[0016] Advantageously, the at least one processing unit is configured to: • Generate a first phase image and a first magnitude image from a first reference image from the first image of said type, • Generate a second phase image and a second magnitude image from a second reference image derived from the second image of said type, • Generate a fat image from the first and second phase images and the first and second magnitude images, • determine, from the fat image, second data on the location of adipose tissue.

[0017] By fat image is meant an image comprising only information on adipose tissue. In other words, the high intensity pixels of the fat image correspond to the location of adipose tissue. Thus, advantageously, by processing the first reference image and the second reference image, corresponding to two different echo durations, it is possible to obtain the second adipose tissue location data.

[0018] Advantageously, the at least one processing unit is configured to: • when determining said first heart lesion location data and said second adipose tissue location data, segment a first reference image from the first white blood image, so as to generate positioning data for a set of at least one wall delimiting the myocardium, • implement a step of geometric characterization of lesions and / or fat in the heart, based on positioning data and second localization data of adipose tissue.

[0019] The segmentation of the first reference white blood image makes it possible to automatically, robustly, reliably and precisely position the walls delimiting the myocardium, because these images present a significant contrast between the myocardium and the blood. The black blood images do not allow obtaining such good results due to the absence of contrast between the healthy myocardium and the blood.

[0020] The geometric lesion and / or fatty characterization of the heart which uses, in addition to the positioning data from the segmentation, the first reference black blood image makes it possible to obtain good results which would not be possible to obtain on its own with the first reference white blood image containing little or no information on the cardiac lesions and the adipose tissues of the heart.

[0021] Advantageously, the at least one processing unit is configured to: • Generate a second fused image from the first fused image and the second adipose tissue localization data, in which lesions of the heart are differentiated from adipose tissue.

[0022] It is thus possible to generate (and therefore subsequently to display and visualize) this second merged image where it is possible to differentiate and / or combine in characteristic formations of fatty tissue of the heart other information characteristic of cardiac lesions.

[0023] Advantageously: • the set of at least one wall comprises a first wall delimiting and surrounding the myocardium, • the at least one processing unit is configured to implement, during the geometric lesion and / or fatty characterization, a preliminary segmentation step to locate an area likely to contain lesions of the myocardium and / or adipose tissue on a first reference black blood image from the first black blood image using data from positioning data of the first wall.

[0024] Advantageously, the first data for locating the myocardial lesion are obtained from data obtained during the preliminary segmentation step and from the second data for locating adipose tissue.

[0025] Advantageously, the at least one output interface comprises a display unit configured to display a first representation of the first location data of the myocardial lesion in a first color and a second representation of the second location data of the adipose tissue in a second color different from the first color.

[0026] The invention also relates to a magnetic resonance imaging system comprising a magnetic resonance system configured to implement an acquisition step and an imaging device as described previously, the acquisition step comprising: • During the inter-beat pair, acquisition of the first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at the said first echo duration, • During the inter-beat pair, acquisition of the first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration, • During the inter-beat pair, acquisition of the second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration.

[0027] Advantageously, the magnetic resonance system is configured to implement: a step of acquiring second signals in white blood by magnetic resonance by late gadolinium enhancement at the second echo duration,

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] and the at least one processing unit is configured to: • generate a second white blood image from the second white blood signals. Advantageously, the magnetic resonance system is configured to implement: • a step of acquiring second black blood signals by magnetic resonance by late gadolinium enhancement at the second echo duration, and in which the at least one processing unit is configured to: • generate a second dark blood image from the second dark blood signals. The invention also relates to an imaging method for imaging the heart of a patient, the heart comprising a myocardium delimiting a cavity of the heart, the method comprising an acquisition step comprising for at least one pair of interbeats comprising two consecutive interbeats: • During the inter-beat pair, acquisition of first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at a first echo duration, • During the inter-beat pair, acquisition of first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration, • During the inter-beat pair, acquisition of second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration, the imaging method further comprising a processing step comprising: • generation of a first black blood image from the first black blood signals, • generation of a first white blood image from the first white blood signals, • generation of a second image of said type from the second signals of said type, • determination, from the first black blood image, the second image of said type and possibly the first white blood image, of first data for locating a myocardial lesion and second data for locating adipose tissue. Thus, the method according to the invention makes it possible to generate, from a minimum of an acquisition of three signals during a pair of consecutive inter-beats, two signals being of the same type among white blood and black blood acquired at two durations different echoes, a combination of information relating to the existence and position of lesions of the myocardium and cardiac adipose tissue. Thus, the method according to the invention allows more precise clinical observations on the basis of a limited amount of information.

[0034] Advantageously, the acquisition step comprises: • Acquisition of second white blood signals by magnetic resonance by late gadolinium enhancement at the second echo duration,

[0035] and the processing step comprises: • generation of a second white blood image from the second white blood signals.

[0036] Advantageously, the acquisition step comprises: • Acquisition of second signals in black blood by magnetic resonance by late gadolinium enhancement at the second echo duration,

[0037] and the processing step comprises: • generation of a second dark blood image from the second dark blood signals.

[0038] Advantageously, the treatment step comprises: • Generation of a first fused image from a first reference white blood image derived from the first white blood image and the first myocardial lesion location data.

[0039] Advantageously, the acquisition step is implemented for a number N greater than 1 of inter-beat pairs so as to generate N first white blood images and N first black blood images, the first reference white blood image being a combination of the N first white blood images and the first reference black blood image being a combination of the N first black blood images.

[0040] Thus, the quality of the first reference white blood image and the first reference black blood image is better, with for example a better signal to noise ratio, and the quality of the first merged image is enhanced.

[0041] Advantageously, the treatment step comprises: • generation of a first phase image and a first magnitude image from a first reference image originating from the first image of said type, • generation of a second phase image and a second magnitude image from a second reference image originating from the second image of said type, • generating a fat image from the first and second phase images and the first and second magnitude images, • determination, by computer, from the fat image, of said second adipose tissue location data.

[0042] By fat image is meant an image comprising only information on adipose tissues. In other words, the high intensity pixels of the fat image correspond to the location of adipose tissues. Thus, advantageously, by processing the first reference image and the second reference image, corresponding to two different echo durations, it is possible to obtain the second location data of adipose tissues of the heart.

[0043] Advantageously, the determination of the first data for locating the heart lesion and the second data for locating the adipose tissues comprises: • segmentation, by computer, of a first reference image from the first white blood image, so as to generate positioning data for a set of at least one wall delimiting the myocardium,

[0044] the method comprising: • geometric characterization of lesions and / or fat in the heart, by computer, from the first reference black blood image, positioning data and second adipose tissue location data.

[0045] The segmentation of the first reference white blood image makes it possible to position the walls delimiting the myocardium automatically, robustly, reliably and precisely, because these images present a significant contrast between the myocardium and the blood. Black blood images do not allow such good results to be obtained due to the absence of contrast between the healthy myocardium and the blood.

[0046] The geometric lesion and / or fatty characterization of the heart which uses, in addition to the positioning data from the segmentation, the first reference black blood image makes it possible to obtain good results which would not be possible to obtain on its own with the first reference white blood image containing little or no information on the cardiac lesions and the adipose tissues of the heart.

[0047] Advantageously, the processing step further comprises generating a second fused image from the first fused image and the second adipose tissue location data, in which the lesions of the heart are differentiated from the adipose tissue.

[0048] It is thus possible to generate (and therefore to display and visualize) this second merged image where it is possible to differentiate and / or combine information characteristic of the adipose tissues of the heart from other information characteristic of cardiac lesions.

[0049] Advantageously: • the set of at least one wall comprises a first wall delimiting and surrounding the myocardium, • the geometric lesion and / or fatty characterization includes a preliminary segmentation to locate an area likely to contain lesions of the myocardium and / or adipose tissue on a first reference black blood image from the first black blood image using data from positioning data of the first wall.

[0050] Advantageously, the first data for locating the myocardial lesion are obtained from data obtained during the preliminary segmentation step and from said second data for locating adipose tissue.

[0051] Advantageously, the method comprises displaying, on a screen, a first representation of the first location data of the myocardial lesion and a second representation of the second location data of the adipose tissue, the first and second representations being displayed in different colors.

[0052] Advantageously, the segmentation of the first reference image uses a first learning function to segment the white blood image so as to obtain the positioning data.

[0053] Advantageously, the first learning function is a convolutional neural network.

[0054] Advantageously, the convolutional neural network is of the Transformer type.

[0055] Advantageously, the first learning function is trained from a set of training images of the area to be imaged generated from signals acquired during respective acquisition stages by white blood magnetic resonance by late gadolinium enhancement distinct from inversion recovery sequences.

[0056] Advantageously, the assembly of at least one wall comprises a first wall delimiting and surrounding the myocardium.

[0057] Advantageously, the preliminary segmentation uses a second learning function to segment the first reference dark blood image from the first dark blood image so as to locate an area likely to contain lesions of the myocardium and / or adipose tissue.

[0058] Advantageously, the second learning function is a convolutional neural network.

[0059] Advantageously, the second learning function is trained from a set of training images of the area to be imaged generated from signals acquired during respective acquisition steps by black blood magnetic resonance by late gadolinium enhancement distinct from inversion recovery sequences.

[0060] Advantageously, the second learning function is trained to determine an intensity threshold.

[0061] Advantageously, the preliminary segmentation comprises the selection of the pixels of the first reference black blood image having an intensity greater than a predetermined threshold, the pixels being taken only from among the pixels of the first reference black blood image surrounded by the first wall.

[0062] Advantageously, the assembly of at least one wall comprises a second wall delimiting the myocardium and surrounded by the first wall.

[0063] Advantageously, the geometric lesion and / or fatty characterization of the heart comprises a step of lesion detection and / or adipose tissue detection, and / or a step of geometric lesion characterization and / or a step of adipose tissue characterization.

[0064] Advantageously, the geometric lesion characterization comprises the calculation of data representative of a lesion size from data originating from the positioning data of the first wall and possibly of a second wall surrounded by the first wall, and from the second data of location of the adipose tissues.

[0065] Advantageously, the geometric lesion characterization comprises the calculation of data representative of a percentage of transmurality of the lesion from data originating from the first lesion location data and the positioning data of the first wall and the second wall.

[0066] Advantageously, the first black blood image, the first white blood image, the second black blood image, the second white blood image, the first reference image and the second reference image are two-dimensional.

[0067] The invention also relates to a magnetic resonance imaging system comprising a magnetic resonance system configured to implement the acquisition step and a processing unit configured to implement the processing step of the method described above.

[0068] Advantageously, the processing unit is configured to implement the step of segmenting the first reference white blood image.

[0069] Advantageously, the processing unit is configured to implement the preliminary segmentation step.

[0070] Advantageously, the processing unit is configured to implement the step of geometric characterization of lesions and / or fat in the heart.

[0071] Advantageously, the system comprises a set of measuring equipment comprising a magnetic resonance imaging device capable of implementing acquisition by magnetic resonance in black blood and acquisition by magnetic resonance in white blood.

[0072] Advantageously, the processing unit is configured to generate commands to the MRI device so that it implements the resonance acquisition. black blood magnetic resonance and white blood magnetic resonance acquisition.

[0073] Alternatively and / or additionally, the processing unit is configured to generate the first (respectively second) white blood and black blood images from the signals acquired during the respective acquisitions.

[0074] Advantageously, the system comprises an electrocardiograph configured to acquire an electrocardiogram of the patient during the respective acquisitions.

[0075] The invention also relates to a computer program product comprising instructions which cause the device according to the invention to implement the processing step by computer.

[0076] The invention also relates to a computer-readable medium, on which the computer program according to the invention is recorded.

[0077] The invention also relates to an imaging method for imaging the heart of a patient, the heart comprising a myocardium, the method comprising an acquisition step comprising for at least one pair of interbeats comprising two consecutive interbeats: • During the inter-beat pair, acquisition of first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at a first echo duration, • During the inter-beat pair, acquisition of first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration, • During the inter-beat pair, acquisition of second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration,

[0078] the imaging method further comprising a processing step comprising: • generation of a first black blood image from the first black blood signals, • generation of a first white blood image from the first white blood signals, • generation of a second image of said type from the second signals of said type, • determination, from the first black blood image, the second image of said type and possibly the first white blood image, of first location data of a first area of interest and second location data of a second area of interest, said first area of interest and second area of interest having on the first black blood image an intensity greater than a predetermined threshold, and corresponding to tissue structures of a different nature.

[0079] Thus, the method according to the invention makes it possible to obtain location information for tissue structures of a different nature from a reference area such as the heart, on the basis of an acquisition of three signals during a pair of consecutive inter-beats, two signals being of the same type among white blood and black blood acquired at two different echo durations. Thus, the method according to the invention allows more precise clinical observations on the basis of a limited quantity of information.

[0080] Among the tissue structures, we can list scars (or lesions), or even fatty tissues.

[0081] Advantageously, the tissue structures corresponding to the first and second areas of interest are areas where gadolinium accumulates preferentially. For example, these are tissue structures consisting of interstitial tissues. Thus, the first and second areas of interest are characterized, in the first black blood image, by an intensity of pixels or voxels greater than a predetermined threshold. Brief description of the figures

[0082] Other characteristics and advantages of the invention will emerge on reading the detailed description which follows, with reference to the appended figures, which illustrate:

[0083] [Fig-1]: an example of an embodiment of a system according to the invention,

[0084] [Fig.2]: a schematic representation of an elementary acquisition sequence of signals for generating first (and / or second and / or other) black blood images and first (and / or second and / or other) white blood images used in the method according to the invention,

[0085] [Fig.3]: a schematic representation of an MRI acquisition phase in black blood and white blood performed over a plurality of heartbeats,

[0086] [Fig.4]: a schematic representation of a heart in three dimensions (3D) illustrating different section planes distributed along the major axis of the heart and images generated from signals acquired in one of the section planes,

[0087] [Fig.5]: a flowchart of an example of a method according to the invention,

[0088] [Fig.6]: a schematic representation of four images comprising at the top on the left a white blood image and on the top right a white blood image on which the walls detected during the segmentation step are represented, and on the bottom left a black blood image and the representation of the walls transferred to the black blood image,

[0089] [Fig.7]: a schematic representation of three images including on the left a black blood image showing the detected lesions and / or fatty tissue of a heart, in the middle a fat image showing the tissues detected adipose tissues, and on the right a second merged image according to the invention on which the detected lesions and the detected adipose tissues are represented in two different representations,

[0090] [Fig.8]: at the top, a black blood image with transferred walls delimiting a lesion, on which sectors have been represented, at the bottom left a bull's-eye type representation of the lesion size and at the bottom right a bull's-eye type representation of a lesion percentage of transmurality. Description of invention

[0091] The invention relates to the field of cardiac imaging by magnetic resonance or MRI, late gadolinium enhancement in black blood and white blood.

[0092] The invention relates to a device, an imaging system and an imaging method for characterizing tissue structures of different nature in or near the heart, and more precisely of at least one cardiac muscle, for example the myocardium.

[0093] In the remainder of this description, and by way of illustration, tissue structures of different nature include cardiac lesions and adipose tissues.

[0094] By cardiac injury is meant an injury to a muscle of the myocardium.

[0095] Cardiac injuries can be divided into acute injuries following a acute myocardial injury, such as acute myocardial infarction, and chronic lesions characteristic of chronic cardiac pathologies. These lesions are cardiac lesions, for example, of the myocardium or papillary muscles. These lesions include myocardial fibrosis, which frequently develops in the context of hypertrophic or dilated cardiomyopathies, but which also represent a frequent sequelae of inflammatory heart disease or myocardial infarction.

[0096] The lesions also include myocardial necrosis, i.e. the volumes of myocytes whose cell membrane has been destroyed and the volumes of extracellular and collagen matrices constituting the fibrous scars, in the chronic phase of the infarction. Imaging system

[0097] [Fig.l] schematically represents an exemplary embodiment of a system S according to the invention. The system comprises the hardware and software means for implementing the method according to the invention.

[0098] Advantageously, this system S comprises a set of measuring equipment A comprising a magnetic resonance imaging (MRI) device B as well as an electrocardiograph referenced ECR in [Fig.l].

[0099] The system S also comprises a processing device C comprising a processing unit TC and a human-machine interface INT. This processing device can be part of the B-imaging device or be external to this device, the system is then a device. Alternatively, the system has a distributed architecture.

[0100] In a manner known per se, the MRI imaging device B comprises a static magnetic field generator GEN_B, a gradient generator GEN_GRAD and a radiofrequency (RF) device D_RF.

[0101] The static magnetic field generator GEN_B comprises a main polarization magnet intended to generate, along a longitudinal axis z, a static magnetic field of substantially uniform polarization in a polarization zone (generally a tunnel) intended to comprise the area of the patient to be imaged, this area to be imaged comprising the heart.

[0102] The patient is a mammal. Without limitation, the mammal is a human.

[0103] The gradient generator GEN_GRAD comprises three gradient coils (or solenoids) arranged and configured to vary the intensity of the magnetic field in the polarization zone along the respective orthogonal axes x, y and z fixed relative to the polarization zone. The choice of the intensities circulating in these coils makes it possible to select, from among several possible ones, a section, having a given thickness and a section plane on which the section is centered, in which the magnetization of the area to be imaged of the patient received in the polarization zone will be measured.

[0104] The radiofrequency device D_RF comprises coils or solenoids and is capable of generating MRI acquisition sequences comprising preparatory sequences for the magnetization of the area to be imaged and sequences for reading RF signals from the area to be imaged.

[0105] Each of the preparatory and reading sequences comprises at least one radiofrequency pulse of predetermined and adjustable frequency, shape, duration, phase and amplitude.

[0106] The preparatory sequence is configured to excite, i.e. modify the direction of magnetization of the tissues in the area to be imaged.

[0107] The reading sequence is configured to measure the magnetization of the area to be imaged resulting from the preparatory module.

[0108] The electrocardiograph ECR is intended to acquire an electrocardiogram of the patient.

[0109] The processing unit TC is configured to generate commands intended for the MRI device B, in particular intended for the RF device D_RF and the gradient generator GEN_GRAD, so that the MRI device generates the predefined acquisition sequences of signals originating from predefined volumes or sections of the area to be imaged.

[0110] The processing unit TC is also configured to generate images of the area to be imaged from the measured signals, from reconstruction techniques known to those skilled in the art, and to process these images as we will see in more detail in the rest of the description. Acquisition sequence

[0111] [Fig.2] represents an example of an elementary acquisition sequence SE1 of an MRI acquisition sequence of RF signals making it possible to generate images of the heart as well as an electrocardiogram (ECG) E measured by the electrocardiograph ECR during the elementary sequence SEL

[0112] The acquisition sequence comprises a series of elementary acquisition sequences SE1 such as that represented in [Fig.2].

[0113] The lower part of [Fig.2] represents the variation of the longitudinal magnetization Mz of the tissues of the area to be imaged as a function of time t during this elementary sequence SEL

[0114] The elementary acquisition sequence SE1 comprises a so-called black blood acquisition ACQ1 followed by a so-called white blood acquisition ACQ2 which will be described later. The black blood acquisition ACQ1 makes it possible to acquire first black blood signals of the area to be imaged making it possible to generate a first elementary black blood image IM1 of the area to be imaged and possibly second black blood signals of the area to be imaged making it possible to generate a second elementary black blood image IM1'. The white blood acquisition ACQ2 makes it possible to acquire first white blood signals of the area to be imaged making it possible to generate a first elementary white blood image IM2 of the area to be imaged and possibly second white blood signals of the area to be imaged making it possible to generate a second elementary white blood image IM2'.These acquisition stages ACQ1, ACQ2 each include a preparatory sequence also called preparatory module PREPI, PREP2 and a preparatory sequence also called reading module LE1, LE2.

[0115] In the present patent application, by module is meant a step comprising a radiofrequency pulse or a series of radiofrequency pulses.

[0116] It should be noted that throughout the duration of the elementary acquisition sequence SE1 and preferably throughout the duration of the MRI acquisition sequence, the static magnetic field generator GEN_B is controlled by the processing unit TC so that it generates a fixed static magnetic field along the z axis.

[0117] The gradient generator GEN_GRAD is controlled for the processing unit TX so that the radiofrequency device D_RF acquires signals coming from a predefined section having a predefined thickness during the elementary acquisition sequence SEL.

[0118] The acquisition sequence is a late gadolinium enhancement acquisition sequence implemented following the injection of a Ga-based contrast agent. gadolinium intravenously in the patient, 10 to 15 minutes before the acquisition sequences are carried out in order to obtain images with maximum contrast between the lesions and healthy tissues and blood. In the heart, the contrast is rapidly eliminated from the healthy myocardium, which is poor in interstitial tissue, but accumulates for a prolonged period in myocardial lesions. Gadolinium has an extracellular distribution, that is, it does not cross the membranes of cardiomyocytes.

[0119] Gadolinium has the effect of shortening the relaxation time Tl of the tissues where it accumulates. The relaxation of the magnetization of the lesions following a magnetization reversal pulse is thus faster than that of healthy blood and myocardium. Black blood acquisition

[0120] Firstly, we seek to generate first elementary images in black blood IML. In an image of this type, the intensity of the pixels corresponding to blood and healthy muscle is zero (black pixels) or substantially zero.

[0121] In order to generate such a first elementary black blood image IM1, the RF device D_RF implements a black blood acquisition step ACQ1 in inversion-recovery. This black blood acquisition step ACQ1 comprises a longitudinal inversion pulse noted 180° in [Fig.2], which switches the longitudinal magnetization of the tissues of the imaged area in the opposite direction, that is to say which reverses the longitudinal magnetization of these tissues. In [Fig.2], it can be seen that the magnetization of the area to be imaged changes from Mz to -Mz under the effect of the inversion pulse. Due to the longitudinal relaxation, the longitudinal magnetization of the different tissues present in the area to be imaged increases to return to its initial value, passing through the zero value. Naturally, the relaxation kinetics of the different tissues are different.

[0122] In a manner known per se, the ACQ1 black blood acquisition also comprises a PREPI preparatory module implemented after the 180° longitudinal inversion pulse, for example, an adiabatic module in Tl-rho (Tlp) of duration noted TSL (acronym for the English expression “Time of Spin Lock”) or a T2-weighted module, or of the MTC type (acronym for the English expression “Magnetization Transfer Contrast”) or a combination of two of these modules or of these three modules.

[0123] The preparatory module PREPI is configured so that the longitudinal magnetization of the blood A(Blood) and that of the healthy myocardium A(Musc) cancel each other out at the same instant te.

[0124] At this same instant te, the longitudinal magnetization of the A(Cica) lesions is clearly greater than zero. By acquiring the signals from the area to be imaged at this instant te, an image is obtained with a very high contrast between the pixels or voxels corresponding to blood and healthy myocardium which are black and the pixels or voxels corresponding to the lesions, generally white.

[0125] The first acquisition step ACQ1 in inversion-recovery then comprises a first reading sequence LE1 comprising a 90° pulse applied at time te and a reading gradient to read the transverse magnetization of the area to be imaged. The inversion time TI is the duration separating the 180° pulse of the first reading sequence LE1 from the elementary sequence ACQL. In order to obtain the best contrast between the myocardial lesions and the blood as well as between the myocardial lesions and the healthy myocardium, the reading sequence LE1 is advantageously started at time te where the longitudinal magnetizations of the blood and the myocardium cancel each other out in order to generate the image having the best contrast.

[0126] In the example of [Fig.2], the first reading module LE1 of the black blood acquisition is temporally spaced from the preparatory module PREPI of the black blood acquisition. Alternatively, the first reading module LE1 begins as soon as the preparatory module PREPI ends. The same applies to the relative temporal positioning between the preparatory module PREP2 of the white blood acquisition and the reading module LE2 of the white blood acquisition.

[0127] In the example of [Fig.2], the inversion pulse IMPI is generated before the preparatory module PREPI. Alternatively, the preparatory module PREPI is generated before the inversion pulse IMP1.

[0128] The first reading module LE1 of the acquisition sequence ACQ1 makes it possible to acquire first black blood signals making it possible to generate a first black blood image IM1.

[0129] In an embodiment called “Black Dixon” which will be detailed later, the second black blood signals are also acquired during the first acquisition step ACQ1, making it possible to generate a second elementary black blood image IM1' by a second reading sequence LE1' of the acquisition sequence ACQ1 starting at an instant having a second echo duration TE2 different from the first echo duration TE1 of the instant at which the first reading sequence LE1 begins.

[0130] By echo duration of a reading sequence, we mean the duration between the instant of the 90° RF pulse applied at the instant te (denoted TE in the state of the art) by the first reading module LE1 and the echo which will be generated by this pulse and which will be recorded by the reading sequence.

[0131] The second elementary black blood image IM1' generated from the second black blood signals is advantageously used with the first elementary black blood image IM1 in an implementation of a Dixon method called “black Dixon” detailed later.

[0132] As will be seen later, the echo durations TE1 and TE2 are predetermined so that signals called water signals and signals called fat signals are respectively in phase (for example, both appearing dark) and out of phase (for example, some appearing light and others appearing dark). The water signals are characteristic of water molecules in the heart and the fat signals are characteristic of adipose tissue. The echo durations TE1 and TE2 depend on the characteristics of the system S, in particular of the MRI imaging device B, and on the characteristics of the 90° pulse applied at the instant te.

[0133] Advantageously, it is also possible to acquire signals making it possible to generate, during the first acquisition step ACQ1, other elementary black blood images at other echo durations different from the first and second echo durations TE1 and TE2. These other echo durations correspond to times when the water signals and the fat signals are either in phase or out of phase. These other elementary black blood images can be used in combination with the first elementary black blood image IM1 and the second elementary black blood image IM1' in an implementation of a Dixon method. White blood acquisition

[0134] The first elementary white blood image IM2 of the area to be imaged is generated from first white blood signals acquired by implementing the white blood acquisition step ACQ2 comprising a preparatory module PREP2 followed by a second reading module LE2 of the white blood acquisition step ACQ2.

[0135] Advantageously, the preparatory module PREP2 is identical to the preparatory module PREPI of the black blood acquisition step ACQ1, but the invention also applies when these modules are distinct.

[0136] The preparatory module PREP2 is, for example, an adiabatic sequence in Tlrho.

[0137] Alternatively, the module PREP2 comprises at least one preparatory sequence taken from a T2-weighted module and a preparatory module of the MTC type (acronym for the Anglo-Saxon expression “Magnetization Transfer Contrast”) or a combination of two of these modules or of these three modules.

[0138] The second reading module LE2 comprising a reading gradient for reading the transverse magnetization of the area to be imaged. This second reading module LE2 can be produced in gradient echo or spin echo, just like the first reading module LE1 of the black blood acquisition step ACQ1. The first and second reading modules LE1 and LE2 can be identical or different.

[0139] The duration D2 separating the second reading module LE2 from the white blood acquisition ACQ2 is defined so that the longitudinal magnetization of the blood A(Blood) is greater than that of the Myocardium A(MUSC) which leads to generating an image in which the pixels or voxels of the blood are white, that is to say with a high luminance, and in which the pixels of the myocardial tissues are a little less bright than those of the blood as can be deduced from the curves represented in [Fig.2]. These images allow the cardiac anatomy to be perfectly visualized, making it possible to delineate the myocardium on this image, which is not possible on a dark blood image.

[0140] The second reading module LE2 of the white blood acquisition ACQ2 makes it possible to acquire first white blood signals making it possible to generate a first white blood image IM2.

[0141] In an embodiment called “White Dixon” which will be detailed later, during the white blood acquisition step ACQ2, second white blood signals are acquired by the second reading module LE2, making it possible to generate a second elementary white blood image IM2' at an echo duration TE2 different from the echo duration TE1 of the instant at which the second reading sequence LE2 begins.

[0142] This second elementary white blood image IM2' is advantageously used with the first elementary white blood image IM2 in an application of a Dixon method called “white Dixon” detailed later.

[0143] As for the acquisition in black blood, the echo times TE1 and TE2 are predetermined so that the water signals and the fat signals are respectively, at the echo time TE1, in phase and, at the echo time TE2, out of phase. The water signals are characteristic of the water molecules in the heart and the fat signals are characteristic of the adipose tissues of the heart.

[0144] For example, the second read sequence LE2 begins with the emission of a 90° RF pulse causing the protons to vibrate, generating echoes at different times subsequent to the time of emission of the 90° RF pulse. For example, the echo durations TE1 and TE2 correspond to the first and second subsequent times.

[0145] The echo times TE1 and TE2 depend on the characteristics of the system S, in particular of the MRI imaging device B, and on the characteristics of the 90° pulse applied at time te.

[0146] Advantageously, during the second acquisition step ACQ2, other elementary white blood images can also be generated at other echo durations different from the echo durations TE1 and TE2. These other echo durations correspond to times when the water signals and the fat signals are either in phase or out of phase. These other elementary white blood images can be used in combination with the first elementary white blood image IM2 and the second elementary white blood image IM2' in an implementation of a Dixon method.

[0147] It will be explained later how, using the images IM1, IM2, and IM1' and possibly the other elementary images in black blood in the “Black Dixon” embodiment, or IM2' and possibly the other elementary images in blood white in the “Dixon white” embodiment, it is possible to improve the detection of lesions and their distinction from the adipose tissues of the heart, and therefore their characterization, in particular their sizing.

[0148] Synchronization of acquisition steps with cardiac cycles

[0149] Preferably, the processing unit TC is configured to synchronize the SE acquisition sequence with electrocardiogram E.

[0150] For this purpose, the processing unit TC uses the electrocardiogram E to generate commands for triggering acquisition sequences for the RF device, the gradient generator and possibly the main magnetic field generator.

[0151] Advantageously, the acquisition sequence SE comprises, as visible in [Fig.3], a plurality of elementary acquisition sequences SEi, with i = 1 to N, where N is greater than 1 where i is the index of the elementary sequence, the acquisition steps ACQ1, ACQ2 of which are identical. i= 1 in [Fig.2].

[0152] Each elementary acquisition sequence SEi is advantageously implemented during two consecutive cardiac cycles, preferably during two consecutive inter-beats C1, C2 referenced in [Fig.2] constituting a pair of inter-beats CBi referenced in [Fig.l]. One advantage is to minimize the acquisition time and therefore to minimize the movements of the heart between the different acquisitions and the spatial shifts between the images IM1 and IM2, and IM1' and IM2', as well as possibly the other elementary images in black blood or the other elementary images in white blood where appropriate.

[0153] In the rest of the text, a beat is called a QRS complex, and an interbeat is a phase of a cardiac cycle located between two consecutive beats.

[0154] Advantageously, the consecutive elementary acquisition sequences SEi are implemented during consecutive inter-beat pairs CBi.

[0155] Each elementary acquisition sequence SEi comprises: - During the first inter-beat Cl of the inter-beat pair CBi, the black blood acquisition step ACQ1; - During the second inter-beat C2 of the CBi inter-beat pair, the white blood acquisition step ACQ2.

[0156] One interest is to minimize the acquisition time and therefore to minimize the movements of the heart between the different acquisitions and the spatial shifts between the images IM1 and IM2, and IM1' and IM2', as well as possibly the other elementary images in black blood or the other elementary images in white blood where appropriate, acquired during the different elementary sequences SEi.

[0157] Advantageously, as shown in [Fig.2], the acquisition sequence SE and the electrocardiogram E are synchronized so that the first and second reading modules LE1, LE2 are implemented during the same phase of respective cardiac cycles Cl, C2.

[0158] Advantageously, this phase is an inter-beat.

[0159] Advantageously, this phase is diastole.

[0160] Advantageously, the acquisition sequence SE and the electrocardiogram E are synchronized so that the reading modules LE1, LE2 of the black blood and white blood acquisition steps ACQ1, ACQ2 are implemented at the same times of these respective cardiac cycles C1, C2.

[0161] These instants are defined in relation to the same time reference of the cardiac cycles C1, C2. The time reference is, for example; the maximum of the QRS complex.

[0162] These instants are separated by the same duration D2' from the maximum of the R wave in the example of [Fig.2].

[0163] The synchronization of the first and second reading modules LE1 LE2 of the black blood and white blood acquisition sequences ACQ1, ACQ2 and, as we will see later, of different reading modules of white blood acquisition steps on the one hand and of the different reading modules of black blood acquisition steps on the other hand, makes it possible to generate images of the heart at times when the heart occupies the same position in a fixed reference frame relative to the main magnet, which makes it possible to superimpose the images obtained without any registration being necessary or by carrying out a simple registration.

[0164] The invention also applies when the order of the acquisition steps is different. For example, it is possible to implement several black blood acquisition steps ACQ1 during consecutive inter-beats then several white blood acquisition steps ACQ2 during consecutive inter-beats and vice versa.

[0165] It is also possible to implement at least one black blood acquisition step ACQ1 and at least one white blood acquisition step ACQ2 during the same interbeat.

[0166] Alternatively, at least one white blood acquisition step ACQ2 is spaced from a temporally closest black blood acquisition step ACQ1. Acquisition of cuts

[0167] Advantageously, the TC processing and control unit is configured to generate commands to the MRI device to acquire signals of respective slices distributed along a predefined axis of the heart.

[0168] The axis is advantageously the long axis of the heart. The sections obtained are then so-called short axis sections. One advantage is that it allows excellent visualization of the two ventricles. However, the invention also applies to the case where the sections are distributed along another axis of the heart, for example an axis in 2 cavities (long axis vertical) or 4 cavities (horizontal long axis) of the core. Each cut has a thickness defined along the axis and is centered on a predefined cutting plane perpendicular to the axis. By cut, in the present application, is meant a slice or layer perpendicular to the g axis and having a predefined thickness along the axis.

[0169] Advantageously, the sections are contiguous along the axis.

[0170] This is achieved by the commands generated by the choice of commands generated to the gradient generator GEN_GRAD by synchronizing the commands to the gradient generator GEN_GRAD and to the RF device D_RF.

[0171] Advantageously, the signals are acquired according to adjacent or partially overlapping sections. This allows the heart to be completely imaged.

[0172] Advantageously, the gradient generator GEN_GRAD is controlled so that several two-dimensional images of each section can be generated, from the signals acquired during the acquisition sequence SE.

[0173] [Fig.4] represents a three-dimensional (3D) view of a heart comprising a plurality of section planes noted PCk distributed along the major axis g, with k= 1 to K, K being an integer greater than 1. The section planes are distributed from the apex AP to the base BA of the heart CO. Consequently, the sections are so-called “minor axis sections”.

[0174] Only the images from the section centered on the cutting plane PCk are represented in [Fig.4].

[0175] Preferably, several first (and possibly second and other) elementary images in black blood IMlk(j) and / or several first (and possibly second and other) elementary images in white blood IM2k(j) are generated with j = 1 to J, J being an integer greater than or equal to 2 for at least one cutting plane PCk, for example for each cutting plane PCk. This makes the analysis of the images obtained more robust.

[0176] Alternatively, the system is configured to acquire signals from a volume, for example from the entire heart so as to enable the generation of three-dimensional images.

[0177] Advantageously, the SE acquisition sequence is implemented while the patient is holding his breath. One advantage is that it obtains perfectly registered images which make it possible to limit, simplify or do without image registration.

[0178] Alternatively, the SE acquisition sequence is implemented in free breathing. Free breathing acquisition has temporal advantages. Indeed, apnea acquisition must be rapid, which means that the acquisition time must be reduced from a few seconds to a few minutes and often involves having to reduce the area to be imaged, for example by limiting it to a 3D portion. However, the sequence SE acquisition being synchronized with the ECG so that the reading sequences are implemented at the same time marker of different cardiac cycles, the apnea acquisition leads to generating a limited number of images. Image generation

[0179] The processing unit TC is configured to generate GEN, by reconstruction techniques known to those skilled in the art, images of the area to be imaged.

[0180] We can, for example, use the GRAPPA algorithm or the SENSE algorithm (and its iterative version).

[0181] The generation step comprises a step of generating two-dimensional (2D) or three-dimensional (3D) elementary images of the area to be imaged from the signals acquired during the SE acquisition sequence.

[0182] In the 2D case, advantageously, for each black blood acquisition step ACQ1, a first black blood image IM1, and possibly a second black blood image IM1' and other black blood images in the "black Dixon" mode, are generated from the signals acquired during the black blood acquisition step ACQ1 and, for each white blood acquisition step ACQ2, a first white blood image IM2, and possibly a second white blood image IM2' and other white blood images in the "white Dixon" mode, from the signals acquired during the white blood acquisition step ACQ2.

[0183] The elementary images IM1, IM2, IM1', IM2' are advantageously generated in gray level. Each image comprises a set of unitary elements of pixel or voxel type, each characterized by an intensity I capable of taking a set of N values (N being a finite integer greater than 1) corresponding to N gray levels ranging from 0 to N-1. For example, this value can take 256 values between 0 and 255, but N is not limited to 256. This value can advantageously take 4096 values between 0 and 4095.

[0184] The TC processing unit is also configured to use the elementary images obtained to characterize lesions of the heart and / or adipose tissue, for example of the myocardium as we will see in more detail in the rest of the text.

[0185] The elementary images generated by the processing unit TC may be intended to be displayed on a screen of the human-machine interface INT. Recalibration

[0186] Advantageously, the method does not include an image registration step.

[0187] Alternatively, the image generation step GEN comprises the registration of first elementary images in black blood with each other, and / or the registration of second elementary images in black blood with each other, and / or the registration of other elementary images in black blood with each other, and / or the registration of first elementary images in white blood. between them, and / or the registration of second elementary images in white blood, and / or the registration of other elementary images in white blood between them, and / or the registration of first (and / or second and / or other) elementary images in black blood and in white blood between them. This makes it possible, in particular when the patient is breathing freely during the SE acquisition sequence, to limit the effects of breathing on the position of the heart and therefore to avoid spatial shifts induced by breathing on the images and likely to affect the precision and reliability of the analyses of these images or of the combination of these images. Indeed, the rhythm of breathing is a priori different from the heart rate, but even if correlations exist between these two rhythms, it is possible that breathing accelerates while the heartbeat remains stable or vice versa.

[0188] Advantageously, the method comprises the registration of first elementary black blood images and / or second elementary black blood images and / or other elementary black blood images of the same section.

[0189] Advantageously, the registration is carried out using a non-rigid image registration algorithm.

[0190] Advantageously, the method comprises the registration of first elementary white blood images and / or second elementary white blood images and / or other elementary white blood images of the same section.

[0191] Advantageously, the registration is carried out using a non-rigid image registration algorithm.

[0192] Advantageously, the method comprises the registration of first elementary images in black blood IM1 and in white blood IM2 between them.

[0193] Advantageously, the method comprises the registration of second elementary images in white blood IM2' and second elementary images in black blood IM1' of the same section.

[0194] Advantageously, the method comprises the registration of other elementary images in white blood IM2'i and other elementary images in black blood IMl'i of the same section.

[0195] Advantageously, this registration is carried out using a non-rigid image registration algorithm.

[0196] Advantageously, these algorithms are identical. It is possible to choose a different algorithm for processing the first, second and other elementary images in black blood and white blood, but it is preferable to choose the same algorithm for ease of implementation. The registration significantly improves the quality, in particular the contrast, of an image resulting from a plurality of first, second and other elementary images of the same section. Furthermore, it makes it possible to reduce artifacts linked to breathing.

[0197] According to a first example, at least one of the non-rigid algorithms is an algorithm based on the method of mutual information between images based on statistical relationships. The function to be optimized can be implemented by a statistical similarity criterion. An advantage of this method is that the matching between homologous attributes of images of the same section is independent of their geometric position. Furthermore, this method is particularly effective for registering first and / or second and / or other elementary images having different contrasts, such as images of black blood and white blood.

[0198] According to a second example compatible with the first example, at least one of the non-rigid algorithms is based on a transformation model is implemented. The transformation model makes it possible to determine functions making it possible to minimize the difference between two images. The difference can be translated by a geometric error to be minimized. Different approaches can be used such as those based on the extraction from each of the images of geometric primitives or shape descriptors such as salient points, shape singularities or contours. A parametric or non-parametric approach can be used.

[0199] According to an example of optimization of a transformation model or a similarity criterion, the least squares method can be used.

[0200] Other optimization methods can be implemented such as gradient descent. However, this latter method applies more particularly to image intensities and is not optimal in the context of the invention since the aim is to optimize the sharpness and contrast of the merged image. Nevertheless, the invention includes this embodiment.

[0201] The registration can be carried out by choosing a reference image and determining a transformation function of the other images of the same section with respect to this image. Each image is then registered by optimizing a transformation to obtain the reference image according to a geometric criterion from the image considered.

[0202] When acquiring three-dimensional images, it is possible to acquire several three-dimensional images of the heart, which can be possibly registered. Combination of images

[0203] When a plurality of elementary images of the same section is generated, it is possible to carry out operations aimed at combining, that is to say merging these images in order to produce a single combined image per section.

[0204] Advantageously, the step of generating the images GEN comprises, for at least one section of index k, for example for each section, the combination of first (or second or other) white blood images of the section IM2k(j), for example for j = 1 to J so as to obtain a first combined white blood image ISBken of the section.

[0205] In the “Dixon white” embodiment, a second combined ISB2ken white blood image of the section is also obtained, and possibly other combined white blood images of the section.

[0206] Advantageously, the method comprises, for at least one section, for example for each section, the combination of first (or second or other) black blood images IMlk(j), for example for j = 1 to J of the section of index k so as to obtain a combined black blood image ISNk of the section.

[0207] In the “Dixon black” embodiment, a second combined ISN2ken black blood image of the section is also obtained, and possibly other combined black blood images of the section.

[0208] This helps reduce noise and increase the signal-to-noise ratio.

[0209] The combination of images can be carried out before, during or after the generation GEN of the images.

[0210] According to one example, the combination is an averaging. The averaging is, for example, performed in the image space or in the Fourier space (i.e., the frequency domain, before reconstruction of the images). These solutions are computationally inexpensive and fast.

[0211] Averaging has the advantage of preserving image detail, since it increases the signal-to-noise ratio (SNR). This technique smooths out noise to reduce residual image artifacts. In addition, averaging improves the bit depth of the digital image beyond what is possible with a single image.

[0212] One advantage of the step of averaging images taken from the same section is to reduce the maximum deviation. The noise amplitude decreases as the square root of the number of images used, i.e. with only 4 images, the noise amplitude can be reduced by a factor of two. According to an example of a free-breathing acquisition lasting 2 min, it is possible to collect 4 to 5 images per section plane, which makes it possible to obtain good noise reduction performance.

[0213] In an exemplary embodiment, the combination of images may alternatively be implemented by motion-compensated iterative reconstruction. In other words, this type of combination is implemented during the reconstruction of the images. Compensated MRI reconstruction techniques are notably described in the following articles: Odille F, et al., “Generalized reconstruction by inversion ofcoupled Systems (GRICS) applied to free-breathing MRI” Magnetic Resonance in Medicine, 2008; and “3D whole-heart isotropy sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST”, Bustin A, et al, Journal of Cardiovascular Magnetic Resonance, 2020.

[0214] Advantageously, the first (or second or other) elementary images of a part in black blood and on the other hand in white blood are respectively combined so as to produce a combined black blood image ISNk and a combined white blood image ISBk per section.

[0215] In the “Dixon white” embodiment, a second combined image ISB2ken white blood of the section is advantageously obtained, and possibly other combined images in white blood of the section.

[0216] In the “Dixon black” embodiment, a second combined image ISN2ken black blood of the section is advantageously obtained, and possibly other combined images in black blood of the section.

[0217] In the following, the term “first reference white blood image” will be understood to mean an image derived from the first white blood image.

[0218] For example, the first reference white blood image may be a combination of N first white blood images (combined or elementary) generated from white blood acquisitions ACQ2 of respective elementary acquisition sequences SEi, with i = 1 to N, where N is greater than 1 and where i is the index of the elementary sequence.

[0219] Alternatively, the first reference white blood image is a white blood image (combined or elementary).

[0220] Similarly, the term “first reference black blood image” will be understood to mean an image derived from the first black blood image.

[0221] For example, the first reference black blood image may be a combination of N first black blood images (combined or elementary) generated from black blood acquisitions ACQ1 of respective elementary acquisition sequences SEi, with i = 1 to N, where N is greater than 1 and where i is the index of the elementary sequence.

[0222] Alternatively, the first reference black blood image is a (combined or elementary) black blood image. The expressions “second reference white blood image” and “second reference black blood image” are defined in the same way, in relation to the second white blood image and the second black blood image, acquired at the second echo duration TE2 of an inter-beat pair.

[0223] When acquiring three-dimensional images, it is possible to acquire several three-dimensional images of the heart, which can be combined, for example averaged, to obtain a single three-dimensional image of the heart.

[0224] Characterization of lesions and / or adipose tissue

[0225] As seen previously, during the acquisition sequence SE, first black blood signals are acquired for the generation of first elementary black blood images IM1 and first white blood signals for the generation of first elementary white blood images IM2.

[0226] Similarly, in the so-called “white Dixon” embodiment, second white blood signals are acquired for the generation of second elementary white blood images IM2' and possibly other elementary white blood images.

[0227] In the so-called “black Dixon” embodiment, second black blood signals are acquired for the generation of second elementary black blood images IM1' and possibly other elementary black blood images.

[0228] These first, second and other elementary images IM1, IM2, possibly IM1', and possibly IM2', are generated from the acquired signals.

[0229] Advantageously, but not necessarily, these first, second, and other elementary images IM1, IM2, IM1', IM2', are generated from signals acquired by implementing an acquisition sequence. This acquisition sequence comprises white blood acquisition steps ACQ2. Each white blood acquisition step ACQ2 is distinct from an inversion-recovery sequence.

[0230] In other words, this sequence is devoid of a pulse of inversion of the longitudinal magnetization of the area to be imaged.

[0231] Consequently, unlike PSIR imaging during white blood acquisition, the longitudinal magnetization of the myocardium is not canceled, which makes it possible to obtain images presenting a stronger contrast between the lesions and the blood and therefore to promote diagnosis and image processing.

[0232] Alternatively, at least one white blood acquisition step is an inversion recovery sequence, for example a PSIR sequence.

[0233] Advantageously, the white blood acquisition is configured so that during the implementation of the reading module LE2, the respective longitudinal magnetizations of the healthy myocardium, the blood and the lesions are positive and that the longitudinal magnetization of the lesions is between the magnetization of the healthy myocardium and that of the blood. This is obtained by the configuration of the preparatory module PREP2 and that of the reading module LE2 and by the relative temporal positioning between these two modules.

[0234] As seen in [Fig.5], the imaging method comprises: - the determination DET, from a first image in black blood, a first image in white blood, a second image of a type taken from white blood in the “white Dixon” embodiment and black blood in the “black Dixon” embodiment, and possibly another image of a type taken from white blood in the “white Dixon” embodiment and black blood in the “black Dixon” embodiment, first data for locating a myocardial lesion and second data for locating adipose tissue.

[0235] This step is implemented by the TC processing unit which uses for this purpose first or second or other white blood images ISB, ISB2, and black blood ISN, ISN2 generated by the method previously described.

[0236] In an embodiment called "fusion mode", it is advantageously possible to generate an image where the first data for locating a myocardial lesion and the second data for positioning adipose tissue are differentiated. In a detailed example later, it will be described that the differentiation can be carried out by using different representative colors or patterns.

[0237] In an embodiment called “segmentation mode”, it is advantageously possible to determine quantitative data on the basis of the first myocardial lesion location data and the second adipose tissue positioning data. In this embodiment, a SEG segmentation step and a preliminary SEG segmentation step are advantageously implemented.

[0238] Each first white blood image ISB, or second white blood image ISB2, or other white blood image respectively black blood ISN, ISN2, ISN2, is a first (or second or other) elementary image IMlk(j), IM2k(j) or a first (or second or other) combined image in white blood ISBk, ISB2k, respectively in black blood ISNk,ISN2k.

[0239] In the remainder of the text, it is considered, as in the example of [Fig.5], that each first white blood image ISB, or second white blood image ISB2, or other white blood image is a first (or second or other) combined white blood image ISBk, ISB2k and that each first black blood image ISN, or second black blood image ISN2, or other black blood image is a first (or second or other) combined black blood image ISNk, ISN2k.

[0240] The invention makes it possible to characterize, in an automatic, reproducible, reliable and precise manner, a cardiac lesion, and more precisely cardiac muscles, in particular lesions of the myocardium, as well as adipose tissue. Segmentation

[0241] According to an example, a SEG segmentation is implemented using at least one image from the first white blood image ISB, or the second white blood image ISB2 in the case of the “White Dixon” embodiment, so as to generate positioning data of a first wall L1 and a second wall L2 delimiting the myocardium M and surrounding and delimiting a cavity of the heart, the first wall L1 surrounding the second wall L2.

[0242] The positioning data relating to a wall correspond, for example, to the identification of the pixels constituting the wall.

[0243] The result of this segmentation is visible in [Fig.6] schematically representing at the top left a first white blood image of a section of the ISBk heart and at the top right the first white blood image of the section on which the first wall L1 and the second wall L2 obtained during the segmentation step are represented in thick black lines.

[0244] The generated images are, for example, in grayscale. The dotted, squared and triangular areas in [Fig.6] represent areas of greater intensity than the bricks.

[0245] In the example of [Fig.6], the cavity of the heart is the left ventricle and the SEG segmentation is implemented so as to delimit the walls L1, L2 of the part of the myocardium surrounding and delimiting the left ventricle.

[0246] It should be noted that in the present description the invention is described in the case where the cavity of the heart is the left ventricle, but the invention is applicable to any cavity of the heart, such as the right ventricle and the atria which are also surrounded and delimited by the myocardium and subject to cardiac lesions.

[0247] The second wall L2 is the wall delimiting the myocardium and the left ventricle LV. The first wall L1 surrounding the second wall L2 is the external wall, i.e. facing the outside of the left ventricle LV, of the part of the myocardium surrounding the left ventricle LV. This is the epicardium.

[0248] The second wall L2 is the wall of the myocardium delimiting the left ventricle. This is the endocardium.

[0249] In the images of [Fig.6] which are sections of the heart, these walls L1, L2 form closed curves in that they completely surround the left ventricle LV in short axis sections.

[0250] It is easy to understand that in 3D these walls form surfaces.

[0251] Alternatively, the segmentation is implemented so as to generate positioning data for only one of these two walls, for example the external wall of the myocardium.

[0252] SEG segmentation is performed by implementing a first learning function or algorithm, for example an artificial neural network, to segment a white blood image so as to delimit at least one wall of the myocardium surrounding and delimiting a cavity of the heart.

[0253] In a non-limiting example, the first learning function is a neural network.

[0254] The artificial neural network used for segmentation is advantageously a convolutional neural network.

[0255] The convolutional neural network is, for example, of the U-Net type or of the transformer type also called a self-attentive model, for example, of the type commonly called swin transformer.

[0256] The neural network, or more generally the first learning function, is implemented on two-dimensional (2D) images and / or on three-dimensional images. ional (3D). In other words, it is trained to perform the desired segmentation by receiving 2D and / or 3D images as input.

[0257] Advantageously, the first learning function is trained, prior to the implementation of the method according to the invention, from white blood images of the heart, generated from signals acquired during respective white blood acquisition steps distinct from inversion recovery sequences, and labeled by specialists, i.e. segmented by specialists, so that the first trained learning function receiving input data comprising a white blood image of the heart, is capable of segmenting so as to delimit at least one wall of the myocardium surrounding and delimiting a cavity of the heart.

[0258] The first learning function is, for example, configured to deliver, from a white blood input image generated from signals acquired during a white blood acquisition step distinct from an inversion recovery sequence, an output image in which the pixels or voxels corresponding to the walls or contours L1 and L2 are colorized in a predetermined intensity or color or in respective predetermined colors. Spread

[0259] The method may comprise a step of propagating the walls detected during the SEG segmentation step, on at least a first black blood image (and / or a second or other black blood image in the “Black Dixon” embodiment). In other words, the method may comprise a REP reporting step, i.e. propagation, comprising, the identification, on a first (or second or other) black blood image ISNk, ISN2k, of the pixels or voxels corresponding to the walls L1 and L2 identified during the SEG segmentation.

[0260] In [Fig.6], a first black blood image ISNk of the heart section is schematically represented at the bottom left and the first black blood image at the bottom right on which the L1 and L2 walls detected during the SEG segmentation step are represented in thick black lines.

[0261] The identification, on the first black blood image ISNk, of the pixels or voxels corresponding to the first wall L1 and respectively to the second wall L2 is determined from the positions of the pixels or voxels corresponding to these walls on the first white blood image ISBk.

[0262] These pixels or voxels may have the same respective positions on the first white blood image and on the first black blood image when we consider that these images are spatially realigned and because these images have the same size and the same resolution.

[0263] A predetermined or calculated spatial shift may alternatively be applied to these pixels or voxels when a spatial shift is estimated to exist between these images.

[0264] The report may include the annotation or coloring of the pixels or voxels corresponding to the L1 and L2 walls.

[0265] Advantageously, the characterization comprises segmentation for each first (or second or other in the “White Dixon” embodiment) white blood image ISBk so as to generate respective positioning data obtained from the respective first (or second or other in the “White Dixon” embodiment) white blood images ISBk.

[0266] Advantageously, the characterization comprises the transfer to each first (or second or other in the “Dixon black” embodiment) black blood image ISNk, of the pixels or voxels corresponding to the walls L1 and L2 identified during the segmentation SEG, from one of the first (or second or other in the “Dixon white” embodiment) white blood images ISBk. The pixels or voxels transferred to the different black blood images or combined black blood images are advantageously identified from respective first (or second or other in the “Dixon white” embodiment) white blood images.

[0267] Advantageously, the positioning data used for the transfer to a black blood image ISNk are generated from a first white blood image ISBk (or from a second or other white blood image ISB2k) generated from signals measured during the same elementary acquisition sequence.

[0268] Thus, the positioning data generated from a combined white blood image ISBk (or from a second or other white blood image ISB2k of order k) are advantageously transferred to a combined black blood image ISNk of order k.

[0269] Lesional and / or fatty geometric characterization step CAR

[0270] The step of geometric characterization of lesions and / or fat CAR consists of characterizing the heart from the point of view of lesions and / or adipose tissues using one or more first (or second or other in the “black Dixon” embodiment) black-blood images ISNk ISN2k, and positioning data of at least one wall, for example of the second wall L2, obtained from one or more first (or second or other in the “white Dixon” embodiment) white-blood images ISBk ISB2k.

[0271] This step advantageously makes it possible to generate data characterizing the heart from the point of view of lesions and / or adipose tissue.

[0272] This step is implemented by the CT processing unit.

[0273] The CAR characterization step may comprise a step of lesion detection and / or DEG detection of adipose tissues, and / or a step of geometric characterization of CAE lesion and / or a step of CAG characterization of adipose tissues of the heart.

[0274] Advantageously, when a lesion is detected, the CAE lesion characterization step is implemented. In other words, the CAE lesion characterization step can be implemented only if a lesion is detected during the DE detection step.

[0275] Alternatively, the DE detection step is implemented after the CAE lesion characterization step or after one of the steps of this effective CAE lesion characterization step.

[0276] Alternatively, the CAE lesion geometric characterization step is devoid of a DE detection step.

[0277] Advantageously, when adipose tissue is detected, the CAG characterization step of adipose tissue of the heart is implemented. In other words, the CAG characterization step of adipose tissue of the heart can be implemented only on condition that adipose tissue of the heart is detected during the DE detection step.

[0278] Alternatively, the DEG detection step of adipose tissues of the heart is implemented after the CAG characterization step of adipose tissues of the heart or after one of the steps of this CAG characterization step of adipose tissues of the heart.

[0279] Alternatively, the CAG characterization step of adipose tissues of the heart is devoid of a DEG detection step of adipose tissues of the heart.

[0280] As mentioned above, in dark blood images, lesions (also called scars) are often difficult to distinguish from tissues such as adipose tissues. Thus, the method according to the invention advantageously makes it possible to detect and characterize, initially, the adipose tissues of the heart from one or more dark blood images, so as to distinguish a posteriori the areas corresponding to one or more lesions.

[0281] Thus, in a preliminary segmentation step SP, an area likely to contain the lesions and / or adipose tissue is detected in at least one black blood image ISNk so as to generate positioning data of at least one cardiac lesion and adipose tissue of the heart possibly present in the figure, the preliminary segmentation SP using positioning data of the first wall L1 and possibly those of the second wall L2, generated during the segmentation step SEG.

[0282] Then, the step of CAG characterization of adipose tissues of the heart and / or DEG detection of adipose tissues of the heart are implemented.

[0283] The results of these steps make it possible to implement the step of characterizing CAE lesions and / or detecting DE lesions.

[0284] The CAE lesion characterization step optionally includes the following steps: - CTA calculation of CIC lesion size from the positioning data of the first wall L1 and possibly the second wall L2 from the SEG segmentation step, and possibly data from the step CAG characterization of adipose tissue and / or DEG detection of adipose tissue - CTT calculation of at least one degree of transmurality TR of the CIC lesion from the first wall L1 and the second wall L2 resulting from the SEG segmentation step.

[0285] These calculations are performed using a set of at least one first (or second and / or other in the “Dixon black” embodiment) black blood image.

[0286] By size of a lesion, we mean data representative of dimensions of the lesion, such as a volume or a surface, for example, or a number of pixels or voxels. Preliminary SP segmentation

[0287] The preliminary segmentation SP uses one or more first (or second or other in the “Dixon black” embodiment) black blood image(s) ISNk, ISN2k, and positioning data of the first wall L1 and possibly those of the second wall L2 resulting from the SEG segmentation.

[0288] These positioning data may be positioning data generated during the segmentation step SEG or positioning data from the reporting step REP. Alternatively, the preliminary segmentation step SP comprises the reporting step.

[0289] This step makes it possible to locate a zone Zc likely to contain both cardiac lesions and adipose tissue, i.e. to generate location data for this zone Zc.

[0290] This location data includes, for example, the identification or positions of the pixels or voxels corresponding to this zone Zc.

[0291] The preliminary segmentation step SP is advantageously implemented by thresholding.

[0292] It advantageously comprises the identification of pixels or voxels having an intensity greater than or equal to a predetermined intensity threshold only in a predetermined area of at least a first (or second and / or other in the “Dixon black” embodiment) black blood image ISNk, ISN2k) delimited by the first wall L1 and / or the second wall L2. Indeed, as can be deduced from [Fig.2], the lesions and the adipose tissues have, on the black blood images, a high intensity compared to the healthy myocardium and to the blood.

[0293] This zone is determined from the positioning data of the first wall L1 and possibly those of the second wall L2 from the SEG segmentation.

[0294] This is for example the area of a first black blood image ISNk delimited by the pixels or voxels of the first wall L1 and / or the pixels or voxels of the second L2 wall reported on this first black blood image ISNk.

[0295] Advantageously, the area of the first black blood image is the area surrounded and delimited by the first wall L1.

[0296] In other words, the preliminary segmentation step SP comprises searching for pixels or voxels of intensity greater than or equal to a predetermined intensity threshold only in the area delimited and surrounded by the first wall L1 on one or more first (or second or other in the embodiment "Dixon "black") black blood image(s) ISNk, ISN2k or ISNik. In other words, these pixels or voxels are taken only from the pixels or voxels of an area of the first (or second or other in the "Dixon "black" embodiment) black blood images surrounded and delimited by the first wall L1. This makes it possible to avoid the erroneous detection of lesions beyond the epicardium, by avoiding confusion between lesions and fat surrounding the epicardium and represented, on the first (or second or other in the "Dixon "black" embodiment) black blood images, by high intensity pixels.

[0297] Alternatively, the zone Z is the zone of the first (or second or other in the “Dixon black” embodiment) black blood images ISNk delimited by the first wall L1 and by the second wall L2.

[0298] In other words, the preliminary segmentation SP comprises searching for pixels with an intensity greater than or equal to a predetermined intensity threshold only in the area delimited by the two walls L1 and L2 of one or more first (or second or other in the “black Dixon” embodiment) black blood images ISNkISN2k. This variant has the advantage of identifying the pixels or voxels of the lesions and / or the adipose tissues of the myocardium only. Indeed, it happens that patients have necrosis of the papillary muscles (located in the area delimited by the wall L2). In these patients the muscles are white on the black blood image which can lead to errors in lesion characterization when segmenting the lesions in the entire area delimited by LL

[0299] Alternatively and / or in addition, the preliminary segmentation SP comprises searching for pixels with an intensity greater than or equal to a predetermined intensity threshold only in the area surrounded by the wall L2. This step makes it possible to identify the pixels or voxels of the papillary muscles only.

[0300] Alternatively, the preliminary segmentation SP is implemented using a second learning function, for example, a neural network, for example, a convolutional neural network trained to segment cardiac lesions in an area delimited by the walls L1 and / or L2 when it receives as input the positioning data of the corresponding wall(s) of the first (or second or other in the “Dixon black” embodiment) black blood image, or using at least least one active contour segmentation algorithm, i.e., a segmentation algorithm using an active contour model.

[0301] A common detection step DEC is implemented, comprising detecting the absence or presence of the zone Z likely to contain lesions and / or adipose tissue using a first (or second or other in the “Dixon black” embodiment) blood-black image ISNk, ISN2k and positioning data of at least one wall, for example, of the second wall L2. This step generates as output an indication of the presence or absence of this zone Z.

[0302] The common detection step DEC can be carried out by thresholding or by using a neural network like the SEG segmentation step. It can consist of determining whether a number of contiguous pixels or voxels greater than a predetermined threshold has an intensity greater than a predetermined threshold in the area delimited by the L1 wall and / or the L2 wall whose positioning is defined during the SEG segmentation step of the myocardium. The presence of the zone Zc likely to contain cardiac lesions and / or adipose tissue is detected if this condition is verified and the absence of this zone Zc is detected if this condition is not verified.

[0303] The neural network is, for example, a convolutional neural network. The neural network is, for example, trained to detect the presence or absence of cardiac lesions in an area delimited by the L1 and / or L2 walls when it receives as input the positioning data of the corresponding wall(s) and the black blood image.

[0304] DEG detection and / or CAG characterization of adipose tissues of the heart

[0305] These steps are implemented from an IG fat image generated from of images generated in the method previously described. These steps make it possible, among other things, to determine second data on the location of fatty tissue.

[0306] The determination of the second adipose tissue location data is implemented by implementing a Dixon method. The Dixon method is generally used to distinguish in images of biological samples the adipose tissues contained therein from the aqueous areas contained therein. It thus makes it possible to obtain, on the one hand, an image containing only the adipose tissues of the biological sample studied, and on the other hand, an image containing only the aqueous areas of the biological sample studied.

[0307] To do this, the method comprises a step of generating GENG a fat image IG comprising a first step GENG1, a second step GENG2 and a third step COMB which will be detailed below.

[0308] The Dixon method uses as input a first image of the sample studied where the adipose tissues and the aqueous zones are in phase, and a second image of the sample studied where the adipose tissues and the aqueous zones are out of phase. In a alternatively, the method also receives at least a third image as input.

[0309] Here we describe the case where only a first image and a second image are received as input.

[0310] In the “White Dixon” embodiment, the first image and the second image consist of the first white blood image ISB, corresponding to an echo time TE1, and the second white blood image ISB2, corresponding to an echo time TE2.

[0311] In the “Dixon black” embodiment, the first image and the second image consist of the first image in black blood ISN, corresponding to the echo time TE1, and the second image in black blood ISN2, corresponding to an echo time TE2.

[0312] Since protons rotate at different speeds in adipose tissue and in water, it is thus possible to find TE1 and TE2 echo times such that the adipose tissue and aqueous areas are either in phase or in antiphase. Typically, the time difference between the TE1 echo time and the TE2 echo time is very small, resulting in differences in the phase information and very small differences in the magnitude information. For example, the TE1 and TE2 echo times are approximately equal to 1.2 ms.

[0313] A first phase image and a first magnitude image are generated during a first step GENG1 from the first image II. A second phase image and a second magnitude image are generated during a second step GENG2 from the second image 12.

[0314] The first phase image, the first magnitude image, the second phase image, and the second magnitude image are then combined during a third COMB step so as to obtain an image called a fat image IG.

[0315] The IG fat image contains only information about the adipose tissues of the sample studied.

[0316] To obtain the IG fat image, the information from the images where the adipose tissues and aqueous areas are out of phase is typically subtracted from the information from the images where the adipose tissues and aqueous areas are in phase.

[0317] For a detailed description of the combination of the first phase image, the first magnitude image, the second phase image, and the second magnitude image to obtain the IG fat image, reference may be made to the article “Multiecho Dixon fat and water separation method for detecting fibrofatty infiltration in the myocardium,” Kellman P, Hernando D, Shah S, et al., Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2009, vol. 61, no. 1, pp. 215-221.

[0318] In the variant where the method also receives a third image as input, the first, the second, and the third images are combined to obtain the IG fat image. An example of a combination is described in the article “Three-point Dixon technique for true water / fat decomposition with b inhomogeneity correction,” GH Glover and E. Schneider, Magnetic Resonance in Medicine, 18, 1991.

[0319] Once the IG fat image has been generated, it is possible to process it in order to detect and characterize the adipose tissues visible there.

[0320] The DEG detection step comprises detecting the absence or presence of adipose tissue using the fat image IG and positioning data of at least one wall, for example, the second wall L2. It generates as output an indication of the presence or absence of adipose tissue.

[0321] The DEG detection step can be performed by thresholding or using a neural network as for the preliminary segmentation step SP. This step can consist of determining whether a number of contiguous pixels or voxels greater than a predetermined threshold have an intensity greater than a predetermined threshold.

[0322] Advantageously, a step of propagation of the walls detected during the SEG segmentation step on the IG fat image can be implemented, so as to differentiate the epicardial adipose tissues from the adipose tissues which have infiltrated inside the myocardium and to focus on the latter during a clinical observation. In this case, a second REP reporting step, i.e. propagation, comprising the identification, on an IG fat image, of the pixels or voxels corresponding to the L1 and L2 walls identified during the SEG segmentation is implemented.

[0323] The DEG detection step advantageously comprises the identification of pixels or voxels having an intensity greater than or equal to a predetermined intensity threshold only in a predetermined area of the fat image delimited by the first wall L1 and / or the second wall L2. In other words, it is determined whether a number of contiguous pixels or voxels greater than a predetermined threshold have an intensity greater than a predetermined threshold in this area. The presence of myocardial-specific adipose tissue is detected if this condition is verified and the absence of myocardial-specific adipose tissue is detected if this condition is not verified.

[0324] The neural network is, for example, a convolutional neural network. The neural network is, for example, trained to detect the presence or absence of adipose tissue of the heart in an area delimited by the walls L1 and / or L2 when it receives as input the positioning data of the corresponding wall(s) and the fat image IG.

[0325] In [Fig.7], in the middle, an image of IG fat is shown, where the pixels where a presence of adipose tissue specific to the myocardium G are represented in diagonal hatching. These pixels are from the DEG detection step implemented on the IG fat image.

[0326] The pixels of the IG fat image where the presence of adipose tissue is detected can be identified. For example, the coordinates of these pixels can constitute the second adipose tissue location data.

[0327] DE lesion detection and / or geometric characterization of CAE lesion

[0328] These steps use the first myocardial lesion location data and the second adipose tissue location data as will be described below.

[0329] In the “fusion mode” embodiment, a first fused image is generated by combining a first reference white blood image as defined above with the first myocardial lesion location data. For example, the first myocardial lesion location data may correspond to the pixel coordinates of a first black blood image whose intensity is greater than a certain value. As mentioned above, in fact, on the black blood images, the contrast is very high between the pixels or voxels corresponding to the blood and healthy myocardium which are black and the pixels or voxels corresponding to the lesions and adipose tissue, which are generally white.

[0330] Then, a second fused image is generated by combining the first fused image with the second adipose tissue positioning data.

[0331] For example, as seen previously, the pixels of the IG fat image where the presence of adipose tissue is detected can be identified by their coordinates which can advantageously constitute the second adipose tissue location data. These pixels can be colored in a first color.

[0332] Also, the pixels corresponding to the first myocardial lesion location data can be colored in a second color different from the first color.

[0333] The white blood images allowing the cardiac anatomy to be perfectly visualized, the second merged image advantageously comprises the cardiac anatomy, pixels or voxels colored in the first color corresponding to adipose tissues, and pixels or voxels colored in the second color corresponding to lesions and / or adipose tissues.

[0334] By playing in particular on the contrast of the first reference black blood image and the fat image, for example by adjusting the detection threshold, it is possible to adjust the distribution of colors in the second merged image in order to accentuate the visualization of this or that structure.

[0335] Thus, in the event of overlapping of certain high intensity pixels of the first reference black blood image with other colored pixels of the fat image, it It is possible to increase the intensity of one color among the first color and the second color in order to preferentially distinguish cardiac lesions (or conversely fatty tissues).

[0336] In the “segmentation mode” embodiment, the first (or second or other in the “Dixon black” embodiment) black blood image(s) ISNk, ISN2k used during the preliminary segmentation step SP, the positioning data of the first wall L1 and possibly those of the second wall L2 from the SEG segmentation, and the data from the DEG detection and / or CAG characterization steps of the adipose tissues of the heart are used.

[0337] For example, the pixels or voxels of the fat image IG where the presence of adipose tissue has been detected can be identified on the first (or second or other in the “Dixon noir” embodiment) black blood image(s) ISNk, ISN2k so as to constitute a first group of pixels or voxels G.

[0338] These pixels or voxels may have the same respective positions on the fat image IG and on the first (or second or other in the “Dixon noir” embodiment) black blood image(s) ISNk, ISN2k used during the preliminary segmentation step SP, when it is considered that these images are spatially realigned and because these images have the same size and the same resolution.

[0339] Thus, the pixels or voxels identified during the preliminary segmentation step SP but not belonging to the first group of pixels or voxels G together form a second group L which can be identified as corresponding to one or more cardiac lesions. The lesion detection step DE may consist of identifying the pixels of this second group.

[0340] In [Fig.7] on the left, we can observe the first image in black blood ISNk of the section of the heart of [Fig.6]. In this image are visible in squares the pixels or voxels identified as having an intensity greater than or equal to a predetermined intensity threshold only in the predetermined zone delimited by the first wall L1 and / or the second wall L2.

[0341] For example, it is possible to annotate or color differently the pixels (or voxels) of the first group G and of the second group L on the first (or second or other in the “Dixon black” embodiment) black blood image(s) ISNk, ISN2k used during the preliminary segmentation step SP.

[0342] In [Fig.7] on the right, we can observe an lopt image obtained from the first black blood image ISNk of the heart section of [Fig.6] following such annotation, in diagonal hatching for the pixels of the first group, corresponding to the presence of adipose tissue, and in squares for the pixels of the second group, corresponding to the presence of lesions. Calculation of lesion size

[0343] The CAE lesion characterization advantageously includes a CTA lesion size calculation step.

[0344] This CTA calculation step comprises the determination of at least one elementary data item representative of the size of at least one cardiac lesion, for example of the myocardium, using location data of the location data of the first and / or second walls L1, L2 which are for example directly the positioning data resulting from the SEG segmentation of the myocardium or else data resulting from these data, for example, data resulting from the transfer step or the positioning data of the lesion obtained during the steps of lesion detection DE and geometric characterization of the lesion CAE.

[0345] In [Fig.8], a first black blood image ISNk is shown, on which the limits L1 and L2 identified during the segmentation SEG and reported, i.e. propagated, on this first black blood image ISNk are shown in thick lines, as well as the pixels identified, during the lesion detection step DE, as being pixels of a lesion.

[0346] An elementary data item representative of a lesion size may be a percentage of a surface area of the myocardium occupied by a lesion on a SEC sector of a first (or second or other in the “Dixon black” embodiment) black blood image ISNk, ISN2k, ISNik or a volume or a mass of the lesion in this SEC sector starting from the axis 1 parallel to the axis p and passing substantially through the center of the cardiac cavity on the black blood image ISNk and delimited by two rays R starting from the axis 1 as visible on the first black blood image ISNk„

[0347] The percentage of the myocardial surface occupied by the lesion in the SEC sector can be calculated from the ratio between the number of pixels corresponding to the lesion in this SEC sector and the number of pixels corresponding to the myocardium in this SEC sector.

[0348] The number of pixels corresponding to the lesion in this SEC sector can be calculated from the location data obtained during the lesion segmentation step or can be calculated directly, during the CTA calculation step, for example by selecting, by thresholding, the number of pixels having an intensity greater than a predetermined threshold in the portion of the SEC sector delimited by the walls L1 and L2 or by the wall LL. The CTA step can comprise the calculation of a data item representative of the lesion size in a SEC sector from several elementary data items representative of the calculated lesion size, in this SEC sector, for several first (or second or other in the “Dixon black” embodiment) black blood images ISNk, ISN2k distributed along the axis p.

[0349] For example, a combination or an average of the elementary data is calculated.

[0350] The volume of lesion on a SEC sector can be calculated from the ratio between the number of pixels corresponding to the lesion on this sector and the number of pixels corresponding to the myocardium on this sector, from the thickness of the slice corresponding to a first (or second or other in the “Dixon black” embodiment) black blood image ISNk, ISN2k, when the image is two-dimensional.

[0351] The size and / or volume are advantageously also calculated from the predetermined resolution of the images.

[0352] It should be noted that the density of the myocardium is 1.06 g / ml. It is therefore considered that the mass of a lesion is substantially equal to the volume of the latter, which makes it possible to evaluate the mass of the lesion.

[0353] The CTA calculation step may, for example, comprise the division of the first (or second or other in the “Dixon black” embodiment) black blood image ISNk, ISN2k into a first predefined number, equal to 12 in the non-limiting example of FIG. 1, predefined number of SEC sectors with the same opening angle oAk pointing towards axis 1 and the calculation of the percentage of the surfaces of the myocardium occupied by the lesion on the different SEC sectors.

[0354] The first number of sectors and the opening angle «U can vary depending on the cutting plane PCk. For example, the closer the cutting plane PCk gets to the apex along the major axis, the more the number of sectors decreases and the opening angle «1^ increases.

[0355] This step can be implemented for different first (or second or other in the “Dixon black” embodiment) black blood images ISNk, ISN2k of different sections centered on respective section planes PCk with k = 1 to K. The method advantageously comprises a step of generation GENR, by computer, for example by the processing unit, of a set of at least one representation of geometric lesion and / or fat characterization data and a step of display AFFD of at least one representation of the set of at least one representation on a screen of the human-machine interface.

[0356] The generation step comprises, for example, the generation of data representative of the result of the DE lesion detection step and / or the DEG detection step of adipose tissues of the heart, i.e. the absence or presence of lesion and / or adipose tissues of the heart and the display step comprises the display of this data.

[0357] As mentioned previously, the lopt image of [Fig.7] on the right, was obtained from the first black blood image ISNk of the heart section of [Fig.6] following an annotation, in diagonal hatching for the pixels of a first group of pixels G, corresponding to the presence of adipose tissue, and in squares for the pixels of a second group L, corresponding to the presence of lesions.

[0358] In another example, the at least one characterization data representation geometric lesion and / or fat comprises a fusion of the data from the first (or second) reference white blood image, the first reference black blood image, and an image called a thresholded fat image resulting from a segmentation of the fat image. Fusion means a superposition of these images.

[0359] In another example, the set of at least one representation advantageously comprises a first REPT representation of the data representative of the lesion size calculated during the CTA calculation step.

[0360] For example, it is possible to generate, as visible in [Fig.8], a bull's eye type representation (also called "Bull's eye" in English terminology) of the percentages or data representative of the percentages of the surfaces of the myocardium occupied by the lesion in different sectors of first (or second or other in the "Dixon black" embodiment) black blood images ISNk, ISN2k taken according to the respective cutting planes PCk.

[0361] The Bull's eye representation is defined by the American Heart Association (AHA) with reference to the Anglo-Saxon expression "American Heart Association" and described in the following article: "Standardized Myocardial Segmentation 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.", Manuel D. Cerqueira et al, Circulation, 2002; 105:539-42.

[0362] The bull's-eye type representation comprises a plurality of concentric circles C separated two by two by crowns C. Each crown CO is assigned to a slice or a set of contiguous slices knowing that the closer the crown CO corresponds to a slice or a set of slices to the apex, the closer it is to the center of the circles. Each crown CO is divided into portions of PSE sectors in which are displayed, as in the example of [Fig.7], the percentages of the surface of the myocardium occupied by a lesion and calculated for the respective sectors of the first (or second or other in the "Dixon black" embodiment) black blood image ISNk, ISN2k of the corresponding slice or combinations, for example averages, of the percentages calculated from the percentages of the surface of the myocardium occupied by a lesion calculated for the sectors of the black blood images of the set of corresponding slices.

[0363] Alternatively and / or additionally, the intensity of the pixels of the different portions of crowns depends on the calculated percentage. The lower this percentage, the higher the intensity of the corresponding crown.

[0364] For example, in [Fig.8], the display is shown in the form of a known bull's-eye type representation of the respective averages of the percentages of the size of the lesion calculated in the respective sectors defined on three sets of contiguous black blood images distributed along the p axis associated with the three respective crowns corresponding respectively to a section of the apex (inner crown), to the mid-ventricle (middle crown) and to the basal zone (outer crowns).

[0365] Portions of sectors associated with a percentage greater than 80% are represented in dotted lines and those associated with a percentage less than or equal to 80% are represented in white.

[0366] When generating a 3D image of the heart, the image is advantageously divided into several layers along the p axis and the same data is calculated as from 2D images from these different layers. Transmurality

[0367] The CAE lesion geometric characterization step advantageously comprises a step of calculating data representative of a percentage of transmurality of a lesion. By percentage of transmurality, we mean the percentage of a thickness of the myocardium occupied by a lesion.

[0368] This CTT calculation step comprises the determination of at least one data item representative of the percentage of transmurality of at least one lesion of the myocardium using location data of a lesion and location data of the first and / or second walls L1, L2 resulting from the segmentation step SEG.

[0369] This data may be a percentage of the thickness of the myocardium occupied by a lesion on a sector of the first (or second or other in the “Dixon black” embodiment) black blood image ISNk, ISN2k starting from axis 1.

[0370] The percentage of the thickness of the myocardium occupied by the lesion on the sector can be calculated from the ratio between a number of pixels corresponding to the thickness of the lesion on this sector and the number of pixels corresponding to the thickness of the myocardium on this sector. The number of pixels corresponding to the thickness of the lesion can be a number obtained from the results of the CAE lesion geometric characterization step or be calculated, for example by thresholding, during the CTT calculation step from the positioning data of L1 and possibly of L2 from the SEG segmentation step.

[0371] The number of pixels corresponding to the thickness of the lesion on a sector can be an average or a maximum of numbers of pixels, corresponding to the thickness of the lesion, calculated according to different radii of the sector.

[0372] The number of pixels corresponding to the thickness of the myocardium on a sector can be an average or a maximum of numbers of pixels, corresponding to the thickness of the myocardium, calculated according to different radii of the sector. These numbers are calculated from positioning data of the walls L1 and L2.

[0373] The CTT calculation step may, for example, comprise dividing the first (or second or other in the “Dixon black” embodiment) image into black blood ISNk , ISN2k into a second predefined number of sectors of the same opening angle a2k pointing towards the center of the cardiac cavity and the calculation of the percentage of the myocardial surfaces occupied by the lesion on the different sectors.

[0374] The second number of sectors and therefore the opening angle a2k can vary depending on the cutting plane PCk. For example, the closer the cutting plane PCk gets to the apex, the more the number of sectors decreases and the opening angle d2k increases.

[0375] Advantageously the second number is greater than the first number.

[0376] The CTT step may comprise the calculation of data representative of the transmurality in a sector from several elementary data representative of the transmurality calculated in this sector for several first (or second or other in the “Dixon black” embodiment) black blood images ISNk, ISN2k distributed along the axis p.

[0377] For example, a combination or an average of the elementary data is calculated.

[0378] This step can be implemented for different first (or second or others in the “Dixon black” embodiment) black blood images ISNk> ISN2k of different sections centered on respective PCk section planes.

[0379] The GENR step advantageously comprises the generation of a REPTR representation of data representative of a percentage of transmurality. The AFFD display step advantageously comprises the display of this representation.

[0380] For example, a bull's-eye type representation of the combinations, for example averages, of percentages of the transmurality of the lesion in sectors of sets of contiguous black blood images ISNk taken according to the respective cutting planes PCk can be generated.

[0381] The intensity of the pixels in this image advantageously, but not necessarily, represents the percentage of transmurality.

[0382] For example, in [Fig.8], the display is shown in the form of a bull's-eye type REPTR representation of the averages of percentages of transmurality of the lesion calculated in the sectors defined on several sets of contiguous black blood images taken according to respective cutting planes distributed along the P-axis.

[0383] As previously, the bull's-eye type representation comprises a plurality of portions of sectors whose intensity corresponds to the combination of the percentage of transmurality calculated for this portion of sector.

[0384] The lower this percentage, the higher the intensity of the corresponding corona. Alternatively and / or in addition, the percentages are displayed in the sector portions.

[0385] The steps previously described for the calculation of lesion size and transmurality can advantageously be carried out for adipose tissues in a manner similar based on second adipose tissue location data. Advantages

[0386] The proposed solution makes it possible to obtain images having sufficient resolution and contrast to detect and precisely characterize tissue singularities, for example lesions, in a reference area such as the heart, in a reliable and reproducible manner, and in particular to discriminate more precisely between tissue singularities of a different nature, such as lesions and adipose tissue.

[0387] For example, it allows, by separating three important pieces of information, namely the anatomy of the heart, the adipose tissues and the lesions, on three separate images, namely respectively the first (or second or other) images in white blood and the first (or second or other) images in black blood and the fat images, to implement an automatic method for characterizing the lesions. This automation allows a significant saving in time and reproducibility compared to the solutions of the prior art.

[0388] The black blood and white blood acquisition sequence of the method according to the invention requires a relatively short acquisition time, in particular when acquiring signals to generate 2D images which involve little calculation. This advantageously makes it possible to implement the acquisition sequence in breath-hold breathing and to limit the movements of the heart between the images and therefore the corrections to be made, which makes it possible to limit the computing resources and the implementation of the method in real time. This also makes it possible to limit the artifacts altering the readability of the images. These artifacts accentuate the difficulty of reconstructing clear and precise images in order to locate and detect the lesion. Furthermore, long MRI acquisitions are uncomfortable for the patient. A duration of 10 to 20 min is considered a very long duration and it is difficult for the patient to remain within the MRI without moving.

[0389] Furthermore, in the case of the generation of 2D images by the method according to the invention, artifacts of an image extracted from a section plane of the 3D image are avoided, which are likely to lead to cases in which it is impossible to discriminate the presence of a potential lesion from the presence of blood located near the muscle. Indeed, in certain cases, the lesion is so close to the blood, it is called sub-endocardial, that it is difficult to know, on images presenting artifacts, whether it is a lesion, blood or an artifact of the image.

[0390] In the case where the tissue singularities are lesions and adipose tissues, these can become entangled in the case of several pathologies, and it is thus interesting to observe this phenomenon, such as, for example, the presence of adipose tissues inside the myocardium, or even the development of adipose tissues around old lesions.

[0391] The device and method according to the invention thus make it possible to improve the precision of clinical observations, and in particular to base a clinical decision with little risk of diagnostic error on the presence or absence of a lesion. Material

[0392] From a hardware point of view, the TC processing unit can be seen as a calculator interacting with computer programs.

[0393] The processing unit TC comprises at least one computer, for example, a microcomputer, a computer network, an electronic component, a tablet, a Smartphone or a personal digital assistant (PDA).

[0394] The processing and control unit TC comprises, for example, a computer, comprising a set of at least one processor, and possibly a memory operationally coupled to the computer.

[0395] The memory comprises, for example, a computer-readable medium. The computer-readable medium is a tangible device readable by a reader of the processing unit, capable of storing electronic instructions and of being coupled to the COI, CO2 communication system.

[0396] In other words, the computer-readable medium is a tangible medium. In other words, it is not a transient signal per se, such as radio waves or other freely propagating electromagnetic waves, such as light pulses or electronic signals. Such a computer-readable storage medium is, for example, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0397] For example, the readable medium is an optical disk, a magneto-optical disk, a read-only memory (ROM), an erasable and programmable read-only memory (EPROM), an electrically erasable and programmable read-only memory (EEPROM), a random access memory (RAM), a magnetic card or an optical card.

[0398] The readable medium may include an operating system and load the programs according to the invention. It includes registers adapted to record parameter variables created and modified during the execution of the aforementioned programs. A computer program comprising software instructions is then stored on the readable medium.

[0399] Alternatively, the program instructions come from an external source and are downloaded via a network. This is particularly the case for applications.

[0400] The processing and control unit comprises a computer, i.e. less electronic data processing circuit designed to manipulate and / or transform data represented by electronic or physical quantities in registers of the evaluation system and / or memories into other similar data corresponding to physical data in the memories of registers or other types of display devices, transmission devices or storage devices.

[0401] The processing unit TC comprises, for example, memories, for storing data, for example the black-blood and white-blood images, operatively coupled to the data processing circuit and a reader adapted to read a computer-readable medium.

[0402] The steps of the method according to the invention are, for example, executed by causing the processing circuits of the processing unit TC to read predetermined programs recorded on hardware such as memories so that their data processing circuits execute calculations, control communications and read and / or write data in memories.

[0403] The characterization is, for example, performed on a processing device, for example a single computer, or on a system distributed between several computers (in particular via the use of cloud computing).

[0404] The processing unit TC comprises at least one computer comprising at least elements listed below: a set of one or more processors (for example at least one central processing unit (CPU) and / or at least one graphics processing unit (GPU) and / or at least one microcontroller and / or at least one digital signal processor (DSP)) ASIC capable of interpreting instructions in the form of a computer program and / or a hardware assembly such as an application-specific integrated circuit (ASIC), an in-situ programmable gate array (FPGA), a programmable logic device (PLD) of programmable logic arrays (PLA), a system on chip (SOC), and / or an electronic card in which steps of the method according to the invention are implemented in hardware elements.

[0405] The invention relates to a computer program product comprising the computer-readable medium containing instructions which, when executed by the processing circuit, cause the system S to implement the steps of the method according to the invention.

[0406] The program product may include the computer-readable recording medium.

[0407] The invention also relates to a computer-readable medium, on which the computer program is recorded.

[0408] Alternatively, the program instructions come from an external source and are downloaded via a network. This is particularly the case for applications. In this case, the computer program product comprises a computer-readable data carrier on which the program instructions are stored or a data carrier signal on which the program instructions are encoded.

[0409] The form of the program instructions is, for example, a source code form, a computer-executable form, or any intermediate form between a source code and a computer-executable form, such as the form resulting from the conversion of the source code via an interpreter, an assembler, a compiler, a linker, or a locator. Alternatively, the program instructions are microcode, firmware instructions, state definition data, integrated circuit configuration data (e.g., VHDL), or object code. The program instructions are written in any combination of one or more programming languages, for example, an object-oriented programming language (C++, JAVA, Python), a procedural programming language (e.g., C language).

[0410] The communication unit comprises at least one communication device enabling communication between the elements of the system and possibly between at least one element of the system and a device external to the system. The communication systems can establish a physical link between elements of the system and / or between an element of the system and a device external to the system and / or a remote communication link (wireless) between elements of the system and / or between an element of the system and a device external to the system.

[0411] For example, the at least one communication device comprises at least one input interface configured to receive external data, such as the first black blood signals, the first white blood signals, the second signals of the type taken from black blood and white blood, the plurality of N first black blood signals by black blood magnetic resonance and the plurality of N first white blood signals by white blood magnetic resonance. These external data can be processed by the processing unit TC during a processing step implemented by the processing unit TC as described in the present application.

[0412] For example, the at least one communication device comprises at least one output interface configured to return data resulting from calculations or from a processing step implemented by the processing unit TC as described in the present application.

[0413] The at least one communication device may comprise any hardware, firmware and / or software suitable for communicating information between elements of the device to which the communication device belongs, for example via a data bus, or to an element external to the device. In order to enable data communication between different devices to which, where applicable, communication devices belong, these devices include firmware and / or software hardware enabling a wired or wireless communication link to be established between them, for example Wi-Fi, Bluetooth, cellular or Ethernet.

[0414] The user interface INT allows a user to enter data or commands so as to be able to interact with the programs according to the invention.

[0415] The user interface INT comprises, for example, an interface and output INTS and an input interface INTE.

[0416] The input interface includes, for example, a keyboard or a pointing interface, such as a mouse, a light pen, a touchpad, a remote control, a voice recognition device, a haptic device.

[0417] The output interface INTS is designed to restore information to a user, in a sensory or electrical manner, such as, for example, visually or audibly. The output interface comprises, for example, a display. The display step AFFD may be a step of restoring information by a means other than a display.

[0418] The output interface INTS may be the input device INTE, for example, in the case of a touch pad.

Claims

1. Claims A device for imaging a patient's heart, the heart comprising a myocardium delimiting a heart cavity, comprising: • At least one input interface configured for: • receive, during a pair of inter-beats comprising two consecutive inter-beats, first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at a first echo duration, • receive, during the inter-beat pair, the first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration, • receive, during the inter-beat pair, second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration, • At least one processing unit (TC) configured to implement by computer a processing step comprising: • generation of a first black blood image (BBI) from the first black blood signals, • generation of a first white blood image (WBI) from the first white blood signals, • generation of a second image of said type from the second signals of said type, • determination (DET), from the first black blood image (ISN), the second image of said type and possibly the first white blood image (ISB), of first data for locating a myocardial lesion and second data for locating adipose tissue, • At least one output interface configured to return said first myocardial lesion location data and said second adipose tissue location data.

2. Device according to claim 1, in which the at least one processing unit (TC) is configured to, during the processing step: • Generate a first fused image from a first reference white blood image derived from the first white blood image and the first myocardial lesion location data.

3. Device according to the preceding claim, in which: • The at least one input interface is configured to receive, for a number N greater than 1 of inter-beat pairs, a plurality of N first black blood signals by black blood magnetic resonance and a plurality of N first white blood signals by white blood magnetic resonance, • The at least one processing unit (TC) is configured to generate N first black blood images and N first white blood images, • the first reference white blood image is a combination of the N first white blood images.

4. Device according to the preceding claim, in which the at least one processing unit (TC) is configured to: • Generate a first phase image and a first magnitude image from a first reference image originating from the first image of said type, • Generate a second phase image and a second magnitude image from a second reference image originating from the second image of said type, • Generate a fat image from the first and second phase images and the first and second magnitude images, determining, from the fat image, said second adipose tissue location data.

5. Device according to one of the preceding claims, in which the at least one processing unit (TC) is configured to: • when determining said first heart lesion location data and said second adipose tissue location data, segment a first reference image from the first white blood image, so as to generate positioning data for a set of at least one wall delimiting the myocardium, • implement a step of lesion and / or fatty geometric characterization (CAR) of the heart, from said positioning data and said second adipose tissue location data.

6. Device according to one of the preceding claims in that they depend on claim 2, in which the at least one processing unit (TC) is configured to: • Generate a second fused image from the first fused image and the second adipose tissue location data, in which the lesions of the heart are differentiated from the adipose tissue.

7. Device according to claim 5 or claim 6 in that it depends on claim 5, in which: • the set of at least one wall comprises a first wall delimiting and surrounding the myocardium, • the at least one processing unit (TC) is configured to implement, during the geometric lesion and / or fatty characterization, a preliminary segmentation step (SP) to locate an area likely to contain lesions of the myocardium and / or adipose tissue on a first reference black blood image from the first black blood image using data from positioning data of the first wall.

8. Device according to the preceding claim, in which said first myocardial lesion location data are obtained from data obtained during the preliminary segmentation step (PS) and said second adipose tissue location data.

9. A device according to any preceding claim, wherein the at least one output interface comprises a display unit configured to display a first representation of the first myocardial lesion location data in a first color and a second representation of the second adipose tissue location data in a second color different from the first color.

10. A magnetic resonance imaging system comprising a magnetic resonance system (A) configured to implement an acquisition step and a device according to one of the preceding claims, said acquisition step comprising: • During said pair of inter-beats, acquisition of said first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at said first echo duration, • During said pair of inter-beats, acquisition of said first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration, • During said pair of inter-beats, acquisition of said second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at said at least one other echo duration different from the first echo duration.

11. Magnetic resonance imaging system according to the preceding claim, in which the magnetic resonance system (A) is configured to implement: • a step of acquiring second white blood signals by magnetic resonance by late gadolinium enhancement at the second echo duration, and wherein the at least one processing unit (TC) is configured to: • generate a second white blood image from the second white blood signals.

12. Magnetic resonance imaging system according to any one of claims 10 or 11, wherein the magnetic resonance system (A) is configured to implement: • a step of acquiring second black blood signals by magnetic resonance by late gadolinium enhancement at the second echo duration, and wherein the at least one processing unit is configured to: • generate a second black blood image from the second black blood signals.

Images