METHOD AND SYSTEM FOR CARDIAC MAGNETIC RESONANCE IMAGING

Abstract

Title: METHOD AND SYSTEM FOR CARDIAC MAGNETIC RESONANCE IMAGING. A cardiac magnetic resonance imaging method, the method comprising an acquisition sequence (ES) including: acquiring signals comprising, during each pair consisting of two consecutive cardiac cycles, successive pairs: acquiring dark blood signals from an area to be imaged of an individual's heart by dark blood magnetic resonance with late gadolinium enhancement, acquiring white blood signals from the area to be imaged by white blood magnetic resonance with late gadolinium enhancement, the white blood signals acquired during successive pairs being acquired by implementing a relaxometry sequence. Figure for the abstract: Fig. 3

Description

Title of the invention: METHOD AND SYSTEM FOR CARDIAC IMAGING BY MAGNETIC RESONANCE Field of the invention

[0001] The invention relates to the field of cardiac magnetic resonance imaging (MRI) by late gadolinium enhancement or LGE, in reference to the Anglo-Saxon expression "Late Gadolinium Enhancement".

[0002] The reference technique for characterizing regional lesions, including myocardial fibrosis, is bright-blood LGE (BBL) late gadolinium enhancement imaging by inversion recovery, such as the PSIR (phase-sensitive inversion recovery) sequence. In this type of imaging, the signal from healthy myocardium is suppressed using inversion-recovery pulses, allowing visualization of a lesion, particularly of the myocardium, which is an area of ​​gadolinium accumulation, i.e., an area where gadolinium is cleared more slowly than in healthy myocardium, with high contrast between the healthy myocardium and the lesion.However, for 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 their automatic, accurate, reliable and robust detection.

[0003] To circumvent this problem, black blood LGE (BL-LGE) imaging techniques have been proposed. These techniques allow for the simultaneous cancellation of signals from healthy myocardium, where gadolinium is rapidly eliminated, and from blood, thus providing high contrast between lesions and healthy myocardium, where gadolinium is rapidly eliminated not only with the blood but also with other blood. These techniques are particularly useful for identifying focal, i.e., localized, lesions.

[0004] However, black blood imaging techniques do not allow for precise localization of myocardial lesions in relation to healthy myocardium, as the contrast between the blood and the lesions of healthy myocardium is not sufficiently high.

[0005] The inventors of the present application have previously proposed in patent application FR2203782 a method for acquiring and merging images in black blood and white blood, enabling the detection and better localization of these lesion areas while maintaining a short acquisition time.

[0006] However, this technique does not allow for the quantification of diffuse changes in the tissues of the area to be imaged, particularly the myocardium. This type of change can occur, in particular, during the acute phase of a cardiac event.

[0007] One object of the invention is to propose a method for limiting at least one of the aforementioned disadvantages.

[0008] To this end, the invention relates to a cardiac magnetic resonance imaging method, the method comprising an acquisition sequence including: • acquire signals comprising, during each pair, consisting of two consecutive cardiac cycles, successive pairs: • Acquire dark blood signals from an area to be imaged in an individual's heart by dark blood magnetic resonance imaging with late gadolinium enhancement, • Acquire white blood signals from the area to be imaged by white blood magnetic resonance imaging using late gadolinium enhancement,

[0009] the white blood signals acquired during successive pairs being acquired by implementing a relaxometry sequence.

[0010] According to one embodiment, the black blood signals are acquired by implementing identical black blood acquisition sequences during successive pairs.

[0011] According to one embodiment, the process comprises: • to process, by computer, signals acquired during successive cardiac cycles, including: • Generate a tissue relaxation time map of the area to be imaged from white blood signals acquired during successive pairs of cardiac cycles.

[0012] According to one embodiment, processing signals acquired during the couplings by computer comprises: • generate dark blood images from dark blood signals acquired during successive pairs, • generate white blood images from white blood signals acquired during successive pairs, • Determine at least one data point relating to a representative area of ​​a cardiac lesion from a black blood reference image derived from at least one of the black blood images and from at least one of the white blood images.

[0013] According to one embodiment, the black blood reference image is a combination of black blood images.

[0014] According to one embodiment, at least one data point is determined from white blood signals acquired during only one of the pairs.

[0015] According to one embodiment, at least one data point is determined from white blood signals acquired during a plurality of pairs.

[0016] According to one embodiment at least one data point is determined from the white blood signals acquired during the pairs.

[0017] According to one embodiment, processing signals acquired during the couplings by computer comprises: • segment, by computer, the reference image in black blood so as to determine the area representative of a cardiac lesion.

[0018] According to one embodiment, determining at least one piece of data relating to the representative area of ​​a cardiac lesion includes: • Generate, by computer, a fused image by replacing pixel or voxel values ​​of a first white blood reference image from white blood signals acquired during at least one of the pairs with new values ​​allowing the representative area of ​​a cardiac lesion to be distinguished from the other tissues of the area to be imaged.

[0019] According to one embodiment, processing, by computer, signals acquired during the pairs comprising: • to segment, by computer, at least one second white blood reference image generated from white blood signals acquired during at least one of the pairs so as to generate positioning data for a set of at least one wall delimiting the myocardium, • segment the reference image into black blood from positioning data of at least one wall of the set of at least one wall delimiting the myocardium, taken from the positioning data of the set of at least one wall delimiting the myocardium, so as to determine the area representative of a cardiac lesion.

[0020] According to one embodiment, the second reference white blood image is segmented using a learning function trained from several sets of training images of the area to be imaged generated from training signals acquired during respective acquisition steps by late gadolinium-enhanced white blood magnetic resonance imaging, each set of training images being obtained from white blood signals acquired by implementing a relaxometry sequence.

[0021] In one embodiment, the second segmentation includes the selection of pixels from the black blood reference image having an intensity greater than a predetermined threshold, the pixels being taken only from among the pixels of the black blood image surrounded by the first wall.

[0022] In one embodiment, the assembly of at least one wall comprises a second wall delimiting the myocardium and surrounded by the first wall

[0023] In one embodiment, the processing includes calculating data representative of a percentage of transmurality of the area representative of a cardiac lesion from data from the positioning data of the first wall and the second wall.

[0024] In one embodiment, the method includes displaying, on a screen, a representation of data determined during processing or an image generated during processing.

[0025] In one embodiment, the black blood and white blood images are two-dimensional.

[0026] The invention also relates to an imaging system configured to implement the method according to the invention.

[0027] The system comprises a processing and control unit and a magnetic resonance system, the processing and control unit being configured to control the magnetic resonance system so that the magnetic resonance system implements the acquisition sequence.

[0028] According to one embodiment, the system comprises a device for measuring at least one signal representative of the individual's cardiac activity, the processing and control unit being configured to control the magnetic resonance system based on measurements of at least one signal representative of cardiac activity generated by the measuring device such that the magnetic resonance system implements the acquisition sequence

[0029] According to one embodiment, the system includes an electrocardiograph, the processing and control unit being configured to control the magnetic resonance system from measurements from the electrocardiograph so that the magnetic resonance system implements the acquisition sequence.

[0030] According to one embodiment, the processing and control unit is configured to implement the computer processing step of the signals acquired during successive cardiac cycles.

[0031] The invention also relates to a computer program product comprising instructions which lead the system according to the invention to execute the steps of the process according to the invention.

[0032] The invention also relates to a computer-readable medium on which the computer program according to the invention is recorded. Brief description of the figures

[0033] Other features and advantages of the invention will become apparent from the following detailed description, with reference to the accompanying figures, which illustrate:

[0034] [Fig-1]: an example of an embodiment of a system according to the invention,

[0035] [Fig.2]: a schematic representation of a first elementary signal acquisition sequence for generating black blood images and white blood images used in the process according to the invention,

[0036] [Fig.3]: a schematic representation of an example of an MRI acquisition sequence in black blood and white blood performed on a plurality of cardiac cycles comprising a plurality of elementary acquisition sequences including that of [Fig.2],

[0037] [Fig.4]: a schematic representation of a heart in three dimensions (3D) illustrating different slice planes distributed along the major axis of the heart and images generated from signals acquired in one of the slice planes,

[0038] [Fig.5]: a flowchart of an example of a process according to the invention,

[0039] [Fig.6]: a schematic representation of four images comprising at the top left an image in white blood and at the top right a white blood image on which are represented the walls detected during the segmentation step, and at the bottom left an image in black blood and the representation of the walls transferred onto the black blood image,

[0040] [Fig.7]: at the top, the image at the bottom left of [Fig.6] on which sectors have been represented, at the bottom left a bullseye-type representation of the size of a representative area of ​​a lesion and at the bottom right a bullseye-type representation of a lesion percentage of transmurality. Invention description

[0041] The invention relates to the field of cardiac imaging by magnetic resonance or MRI, late gadolinium enhancement in black blood and white blood.

[0042] The invention relates to a cardiac imaging method.

[0043] A cardiac lesion is defined as an area exhibiting a tissue peculiarity near or within a reference area represented by the myocardium. This area has a tissue structure that is modified compared to a reference tissue structure, the most representative of the myocardium.

[0044] The modification of the tissue structure results in the contrast agent accumulating for a longer period in the area presenting the tissue singularity than in the areas presenting the tissue structure most representative of the myocardium.

[0045] Thus, a representative area of ​​a cardiac lesion in a black blood image is characterized by a pixel or voxel intensity greater than that of the pixels of the tissue structure most representative of the myocardium.

[0046] For example, this intensity is greater than a predetermined threshold.

[0047] A distinction is made between pathological and benign lesions. For example, some lesions can affect cardiac function. These lesions are referred to here as pathological. In another example, lesions may not affect cardiac function, such as fatty tissue. The location of areas indicative of cardiac lesions can therefore be of interest to specialists, as they may be an indicator of cardiac pathology affecting cardiac function, such as myocardial scarring, fatty tissue, or other types of tissue changes in the heart or myocardium. This data should be cross-referenced with other information to identify the nature of this particularity. Imaging system

[0048] Figure 1 schematically represents an example of an embodiment of an imaging system according to the invention. The system comprises the hardware and software means for implementing the method according to the invention.

[0049] Advantageously, this system S comprises a set of measurement equipment A including a magnetic resonance (MRI) system B and an electrocardiograph referenced ECR on [Fig.1].

[0050] The system S also includes a processing and control system C comprising a processing and control unit TC and a human-machine interface INT.

[0051] In a manner known per se, the MRI B magnetic resonance system comprises a static magnetic field generator GEN_B, a gradient generator GEN_GRAD and a radio frequency (RF) system D_RF.

[0052] The static magnetic field generator GEN_B includes a main polarizing magnet intended to generate, along a longitudinal axis z, a substantially uniform static magnetic field of polarization in a polarization zone (generally a tunnel) intended to include the area of ​​the patient to be imaged, this area to be imaged including the heart.

[0053] The patient is a mammal. Without limitation, the mammal is a human.

[0054] The GEN_GRAD gradient generator 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 with respect to the polarization zone. The choice of intensities circulating in these coils allows the selection, from several possibilities, of a slice, having a given thickness and a cutting plane on which the slice is centered, in which the magnetization of the area to be imaged of the patient received in the polarization zone will be measured.

[0055] The radio frequency system D_RF comprises a radio frequency transmitter including coils or solenoids and is capable of generating radio frequency (RF) pulse sequences, also referred to as acquisition sequences in this patent application, comprising preparatory sequences for magnetizing the area to be imaged and readout sequences during which an RF radio frequency receiver of the radio frequency system reads, that is to say receives RF signals from the area to be imaged and generated under the effect of radio frequency sequences emitted by the transmitter.

[0056] The radio frequency receiver may be the radio frequency transmitter or be different from the radio frequency receiver. Each of the preparatory and playback sequences includes at least one radio frequency pulse of predetermined and adjustable frequency, shape, duration, phase, and amplitude.

[0057] The preparatory sequence is configured to excite, that is to say, to change the direction of the magnetization of the tissues of the area to be imaged.

[0058] The reading sequence is configured to allow the radio frequency system receiver to measure the magnetization of the area to be imaged resulting from the preparation module. The ECR electrocardiograph is intended to acquire an electrocardiogram of the patient.

[0059] The TC processing and control unit is configured to generate commands for the MRI system B, specifically for the RF system D_RF and the gradient generator GEN_GRAD, so that the MRI device generates predefined acquisition sequences of signals from predefined volumes or slices of the area to be imaged. The TC processing and control unit is configured to process RF signals acquired by the radio frequency system. This processing includes, for example: generating a relaxation time map of the area to be imaged and generating images of the area to be imaged, using reconstruction techniques known to those skilled in the art, and determining data from these images, as will be seen in more detail later in the description. Acquisition sequence

[0060] Figure 2 represents an example of a first elementary acquisition sequence (Sel) of RF signals. This first elementary acquisition sequence (Sel) is one of the elementary sequences of an acquisition sequence (SE) of RF signals, enabling the generation of images of the core and, more precisely, of an area to be imaged.

[0061] Fig. 2 also represents an electrocardiogram (ECG) E measured by the ECR electrocardiograph during the first elementary acquisition sequence Sel.

[0062] The area to be imaged includes an area of ​​the patient's heart myocardium and blood.

[0063] Advantageously, the area to be imaged includes at least one section of the myocardium of the patient's heart taken in a plane and the section, taken in the same plane of at least one cavity surrounded by the myocardium within which the blood circulates as well as an area surrounding the myocardium.

[0064] As we will see later, the acquisition sequence SE comprises a series of N elementary acquisition sequences Sei, with i= 1 to N, i being an integer.

[0065] N is, for example, between 2 and 20 but N can be greater than 20.

[0066] For example, N is equal to 5.

[0067] A first elementary sequence Sel is represented in [Fig.2].

[0068] 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 the first elementary sequence Sel.

[0069] The first elementary acquisition sequence Sel comprises a so-called black blood acquisition ACQ11 followed by a so-called white blood acquisition ACQ21, which will be described later. The black blood acquisition ACQ11 acquires the signals from the area to be imaged, allowing the generation of an elementary black blood image IM1, that is, a black blood contrast image of the area to be imaged.

[0070] The ACQ21 white blood acquisition allows the signals of the area to be imaged to be acquired, enabling the generation of an elementary image in white blood IM2, i.e. in blood-white contrast of the area to be imaged.

[0071] These acquisition steps in black blood ACQ11, and in white blood ACQ21 each include the emission of electrical pulse sequences comprising a preparatory module PREPI, PREP21 and a reading module LE1, LE2.

[0072] In this patent application, the term "module" means a radio frequency pulse or a sequence of radio frequency pulses.

[0073] It should be noted that during the entire duration of the first elementary acquisition sequence Sel and, preferably, during the entire duration of the MRI acquisition sequence or the N elementary acquisition sequences, the static magnetic field generator GEN_B is controlled by the processing and control unit TC to generate a fixed static magnetic field along the z-axis.

[0074] The gradient generator GEN_GRAD is advantageously controlled by the processing and control unit TC so that the radio frequency system D_RF acquires signals from a predefined section having a predefined thickness during the first elementary acquisition sequence Sel and throughout the duration of each of the N elementary sequences Sei with i=l to N.

[0075] In other words, the signals acquired during the SE acquisition sequence come from the same slice, which then constitutes the area to be imaged.

[0076] The first acquisition sequence is a late gadolinium enhancement acquisition sequence implemented following the intravenous injection of a Gadolinium-based contrast agent into the patient, 10 to 15 minutes before the implementation of the acquisition sequences in order to obtain images with maximum contrast between lesions and blood but also between lesions and healthy myocardium or areas of tissue structure most representative of the myocardium.

[0077] At the level of the heart, the contrast agent is rapidly eliminated from the healthy myocardium, which is poor in extracellular or interstitial tissue, but accumulates in a prolonged manner in Myocardial lesions. Indeed, gadolinium has an extracellular distribution, meaning that it does not cross the membranes of cardiomyocytes.

[0078] 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 blood and healthy myocardium. Acquisition in black blood

[0079] Initially, we seek to generate elementary images in black blood IML. In an image of this type, the intensity of the pixels corresponding to the blood and the healthy myocardium or reference area of ​​the myocardium is zero (black pixels), substantially zero or very low.

[0080] In order to generate such an elementary image in black blood IM1, the RF system D_RF implements, during the first elementary acquisition sequence Sel, an inversion-recovery black blood acquisition step ACQ11. This ACQ11 black blood acquisition step includes the emission, by the transmitter of the radio frequency system, of a longitudinal inversion pulse, denoted 180° in [Fig. 2], which flips the longitudinal magnetization of the tissues in the imaged area in the opposite direction, i.e., it 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 imaged area increases to return to its initial value, passing through zero. Naturally, the relaxation kinetics of different tissues are different.

[0081] In a manner known per se, the ACQ11 black blood acquisition also includes a PREPI preparatory module implemented after the 180° longitudinal inversion pulse, for example, an adiabatic Tl-rho (Tlp) module of duration denoted TSL (also called "Spin Lock Time" in Anglo-Saxon terminology) "Time of Spin Lock") or a T2-weighted module, or of the MTC type (acronym for the Anglo-Saxon expression "Magnetization Transfer Contrast") or a combination of two or three of these modules.

[0082] The PREPI preparatory module is configured so that the longitudinal magnetization of blood A(B) and that of healthy myocardium A(M), or more generally of the reference zone of the myocardium, cancel each other out at the same instant te.

[0083] At this same instant te, the longitudinal magnetization of the lesions A(C) is clearly greater than zero. By acquiring the signals from the area to be imaged at this instant te, we obtain an image exhibiting a very high contrast between the pixels or voxels corresponding to the blood, which are generally black, and the pixels or voxels corresponding to the lesions, which are generally white.

[0084] The first ACQ11 inversion-recovery acquisition step then comprises an LE1 readout sequence including a 90° pulse applied at time te and a readout gradient to read the transverse magnetization of the area to be imaged. The inversion time TI is the time between the 180° pulse of the LE1 readout sequence and the ACQ11 dark-blood acquisition. In order to obtain the best contrast between myocardial lesions and blood, as well as between myocardial lesions and healthy myocardium, the LE1 readout sequence is advantageously started at time te when the longitudinal magnetizations of the blood and myocardium cancel each other out, in order to generate the image with the best contrast.

[0085] In the example in [Fig. 2], the LE1 readout module of the black blood acquisition is temporally separated from the PREPI preparatory module of the black blood acquisition. Alternatively, the LE1 readout module begins as soon as the PREPI preparatory module ends. The same applies to the relative temporal positioning between the PREP21 preparatory module of the white blood acquisition and the LE2 readout module of the white blood acquisition.

[0086] In the example of [Fig.2], the IMPI reversing pulse is generated before the PREPI preparatory module. Alternatively, the PREPI preparatory module is generated before the IMP1 reversing pulse. Acquisition in white blood

[0087] The elementary white blood image IM21 of the area to be imaged is generated from acquired signals by implementing, during the first elementary acquisition sequence Sel, the white blood acquisition step ACQ21 comprising a preparatory module PREP21 followed by a reading module LE2.

[0088] The ACQ21 white blood acquisition step then includes a LE2 readout module comprising a readout gradient for reading the transverse magnetization of the area to be imaged. This LE2 readout module can be implemented using gradient echo or spin echo, just like the LE1 readout module of the LE1 black blood acquisition step. The LE1 and LE2 readout modules can be identical or different.

[0089] The time interval D2 separating the reading module LE2 from the preparatory module is defined such that the longitudinal magnetization of blood A(B) is greater than that of the myocardium, particularly healthy myocardium A(M). This results in an image in which the blood pixels or voxels are white, i.e., with high luminance or intensity, and in which the myocardial tissue pixels are slightly less luminous than those of the blood, as can be deduced from the curves shown in [Fig. 2]. These images allow for perfect visualization of the cardiac anatomy, enabling the myocardium to be delineated in this image, which is not possible with a black blood image.

[0090] It is understood that by using the two images IM11 and IM21 it is possible to detect areas of gadolinium accumulation or lesions and also to locate them precisely in relation to the myocardium and to size them in relation to the myocardium. Synchronization

[0091] Preferably, the TC processing and control unit is configured to synchronize the SE acquisition sequence with the E electrocardiogram.

[0092] For this purpose, the TC processing and control unit advantageously uses the electrocardiogram E to generate trigger commands, i.e. implementation commands, for the acquisition sequences destined for the RF device, the gradient generator and possibly the main magnetic field generator.

[0093] The acquisition sequence SE comprises, as seen in [Fig. 3], a plurality of elementary acquisition sequences Sei, with i = 1 to N, where N is greater than 1 and i is the index of the elementary sequence, whose acquisition steps in black blood ACQ1 are identical. i = 1 in [Fig. 2]. We will see later that the acquisition steps in white blood ACQ2i of the different elementary acquisition sequences Sei are different.

[0094] Each elementary acquisition sequence Sei is implemented during two consecutive cardiac cycles Cli, C2i, referenced in [Fig. 2] (where i = 1), constituting a pair CBi of two consecutive cardiac cycles referenced in [Fig. 3]. Advantageously, each elementary acquisition sequence Sei is implemented during two consecutive RR intervals.

[0095] Advantageously, these two elementary acquisition sequences Sei are implemented during two consecutive interbeats constituting a pair of consecutive interbeats.

[0096] One advantage is to minimize the acquisition time and therefore to minimize the risks of heart movements between the different acquisitions and the spatial shifts between the IM1 and IM2i images generated from the signals acquired during the respective elementary acquisition sequences Sei. In the following text, a beat is defined as a QRS complex, and an interbeat as a phase of a cardiac cycle located between two consecutive beats.

[0097] Advantageously, the consecutive elementary acquisition sequences Sei are implemented during pairs of consecutive interbeats CBi as in the example of [Fig.3].

[0098] Each elementary acquisition sequence Sei comprises: - During the first cardiac cycle Cli of the pair of cardiac cycles CBi, the black blood acquisition step ACQli; - During the second cardiac cycle C2i of the pair of cardiac cycles CBi, the white blood acquisition step ACQ2i.

[0099] 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 IM1 and IM2 images acquired during the different elementary Sei sequences.

[0100] Advantageously, as shown in [Fig.2], the acquisition sequence SE and the electrocardiogram E are synchronized so that the reading modules LE1, LE2 are implemented during the same phase of two consecutive cardiac cycles Cli, C2i.

[0101] Advantageously, this phase is an interbeat.

[0102] Advantageously, this phase is diastole.

[0103] Advantageously, the SE acquisition sequence and the E electrocardiogram are synchronized so that the LE1, LE2 readout modules of the black blood and white blood acquisition steps ACQ1, ACQ2i are implemented at the same times of these respective cardiac cycles. C1, C2i.

[0104] These instants are defined with respect to the same temporal reference of cardiac cycles Cli, C2i. The temporal reference is, for example, the maximum of the QRS complex.

[0105] These instants are separated by the same duration D2' from the maximum of the wave R in the example of [Fig.2].

[0106] Synchronization of the LE1, LE2 reading modules of the ACQli, ACQ2i black blood and white blood acquisition sequences and, as we will see later, of different reading modules of white blood acquisition stages on the one hand and of the different reading modules of black blood acquisition stages 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 with respect to the main magnet which makes it possible to superimpose the images obtained without the need for registration or by performing a simple registration.

[0107] As seen in [Fig.3], successive elementary acquisition sequences Sei are implemented during successive pairs CBi with i= 1 = N of two consecutive cardiac cycles.

[0108] In the non-limiting example of [Fig.3], N=3.

[0109] Advantageously, the acquisition sequence SE comprises a plurality of elementary acquisition sequences Sei implemented during consecutive pairs CBi of two consecutive cardiac cycles.

[0110] In a particular embodiment, the elementary acquisition sequences are implemented during consecutive pairs of two consecutive cardiac cycles.

[0111] One advantage is to limit as much as possible the spatial differences between the images generated from the signals acquired during the different elementary acquisition sequences and the acquisition time.

[0112] One advantage is to acquire all the signals corresponding to a slice, in the case of 2D acquisition, in the shortest possible time, which makes it possible to generate the data associated with this slice and, for example, to display at least one image associated with this slice, that is to say, to generate usable results even if the acquisition is interrupted, for example in the event of apnea stopping. Relaxometry

[0113] According to the invention, the white blood signals acquired during the different pairs of cardiac cycles are acquired by implementing a relaxometry sequence.

[0114] In other words, the acquisition sequence composed of N ACQ2i white blood acquisition sequences with i= 1 to N is a relaxometry sequence.

[0115] In this way, it is possible to generate a tissue relaxation time map of the area to be imaged from said white blood signals acquired during the N pairs of cardiac cycles.

[0116] One advantage is to acquire, in a single acquisition sequence and therefore in a limited time, signals allowing not only the detection and localization of an area representative of a cardiac lesion but also the quantification of diffuse changes in myocardial tissues.

[0117] This makes it possible to limit the workload of the technicians and to obtain good quality and consistent data, by limiting the spatial misalignment between the images generated from the signals acquired during the acquisition sequence.

[0118] By relaxometry sequence, we mean a sequence composed of successive acquisition sequences differing by predefined characteristic durations in order to allow the acquisition of signals enabling the generation of the relaxation time map.

[0119] In the example in Figures 2 and 3, each preparatory module PREP2i is, for example, an adiabatic sequence in Tlrho with a duration denoted TSLi.

[0120] Successive ACQ2i white blood acquisition sequences differ only in spin-lock durations, also called TSL, an acronym for the Anglo-Saxon expression Time of spin-lock.

[0121] The order i of each ACQ2i acquisition sequence represents the temporal order of implementation of the sequence. Thus, the first acquisition sequence of order 1, Sel, is implemented first and the acquisition sequence of order N, SEN, is implemented last.

[0122] For example, the TSLi spin-lock durations of the different elementary acquisition sequences are distributed over the range from 0 ms to 50 ms.

[0123] Generally, the locking times are advantageously between 0 ms and 50 ms or between 0 ms and 100 ms.

[0124] For example, in the case where N = 5, the TSLi spin-lock durations include 0 ms, 10 ms, 20 ms, 35 ms, 50 ms.

[0125] In the example of [Fig.3], the spin-lock time TSLi of the ACQ2i white blood elemental acquisition sequence increases monotonically with the order i of the ACQ2i acquisition sequence.

[0126] Alternatively, the spin-lock time varies differently depending on the order i.

[0127] Thus the signals acquired by implementing the different elementary acquisition sequences in ACQ2i white blood make it possible to generate a map of the relaxation times Tlp or Tlrho also called spin-lattice relaxation time of the tissues of the area to be imaged from said white blood signals acquired during the N pairs of cardiac cycles.

[0128] Alternatively, the PREP2i preparatory module of each ACQ2i white blood acquisition sequence is a T2-weighted module.

[0129] In this case, the different preparatory modules of the different acquisitions are configured so that these sequences differ in the durations of the preparatory modules in T2.

[0130] Thus, the signals acquired by implementing the different elementary acquisition sequences in ACQ2i white blood make it possible to generate a map of the T2 relaxation times of the tissues of the area to be imaged from said white blood signals acquired during the N pairs of cardiac cycles.

[0131] Advantageously, the elementary acquisition steps in black blood ACQli of the different elementary acquisition sequences Sei for i= 1 to N are identical.

[0132] Advantageously, the durations of the preparatory modules in T2 are between 0 ms and 100 ms. Acquisition of cups

[0133] Advantageously, the TC processing and control unit is configured to generate commands to the MRI system to acquire signals from respective slices distributed along a predefined axis of the heart.

[0134] To this end, the acquisition step of the process includes a plurality of SE acquisition sequences such as the one previously described to acquire signals from several slices, knowing that during each SE acquisition sequence signals are acquired from a single slice.

[0135] The acquisition sequences dedicated to the different slices are advantageously placed successively.

[0136] Alternatively, the slice acquisition sequences are interlaced.

[0137] In other words, the acquisition step comprises an elementary acquisition sequence of an acquisition sequence of a first slice followed by an elementary acquisition sequence of an acquisition sequence of a second slice followed by another elementary acquisition sequence of the acquisition sequence of the first slice.

[0138] Advantageously, the axis is the major axis of the heart. The resulting slices are then called minor axis slices. One advantage is that they allow excellent visualization of both ventricles. However, the invention also applies to cases where the slices are distributed along another axis of the heart, for example, a two-chamber (vertical long axis) or four-chamber (horizontal long axis) axis. Each slice has a defined thickness along the axis and is centered on a predefined cutting plane perpendicular to the axis. In this application, a slice is defined as a slice or layer perpendicular to the axis and having a predefined thickness along the axis.

[0139] Advantageously, the cuts are contiguous along the axis.

[0140] This is achieved by choosing the commands generated for the gradient generator GEN_GRAD by synchronizing the commands for the gradient generator GEN_GRAD and for the RF system D_RF.

[0141] Advantageously, the signals are acquired according to adjacent or partially overlapping slices. This allows complete imaging of the heart.

[0142] Advantageously, the gradient generator GEN_GRAD is controlled so that several two-dimensional images of each slice can be generated from the signals acquired during the SE acquisition sequence.

[0143] Figure 4 represents a three-dimensional (3D) view of a core comprising a plurality of section planes, denoted 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 core CO. Consequently, the sections are referred to as "minor axis sections".

[0144] Only the images from the section centered on the cutting plane PCksont are represented on [Fig.4].

[0145] Preferably, several elementary images of black blood IM1 and / or several elementary images of white blood IM2i are generated, with i = 1 to N, N being an integer greater than or equal to 2, for at least one section plane PC, for example, for each section plane PC. This makes the analysis of the obtained images more robust.

[0146] Alternatively, the system is configured to acquire signals from a volume, for example from the entire core so as to allow the generation of three-dimensional images.

[0147] Advantageously, the SE acquisition sequence is implemented while the patient is holding their breath. One advantage is obtaining perfectly registered images, which makes it possible to limit, simplify, or eliminate the need for image registration.

[0148] Alternatively, the SE acquisition sequence is performed during free breathing. Free breathing acquisition offers temporal advantages. Indeed, apnea acquisition must be rapid, which necessitates reducing the acquisition time from a few seconds to a few minutes and often implies having to reduce the area to be imaged, for example, by limiting it to a 3D portion. However, since the SE acquisition sequence is synchronized with the ECG so that the reading sequences are performed at the same time marker of different cardiac cycles, apnea acquisition leads to the generation of a limited number of images. Image generation

[0149] The system is configured so that the TC processing and control unit receives the signals acquired during the SE acquisition step.

[0150] More generally, the system is configured so that the TC processing and control unit receives the signals acquired during the acquisition stage.

[0151] The TC processing and control unit is configured to implement, by computer, a TRA processing step of the signal(s) acquired during the SE acquisition sequence, as shown in [Fig.5].

[0152] The TC processing and control unit can be configured to implement a general processing step TRAG which may include one or more TRA processing steps applied to signals acquired during different acquisition sequences SE of the acquisition step.

[0153] The elementary processing step TRA includes the generation GEN, by reconstruction techniques known to those skilled in the art, of images of the area to be imaged.

[0154] One can, for example, use the GRAPPA algorithm or the SENSE algorithm (and its iterative version).

[0155] The generation step GEN includes 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 acquisition sequence SE.

[0156] Advantageously, for each ACQli black blood acquisition step, an IMli black blood elementary image is generated from the signals acquired during the ACQli black blood acquisition step and, for each ACQ2i white blood acquisition step, an IM2i white blood elementary image is generated from the signals acquired during the ACQ2i white blood acquisition step.

[0157] The elementary images IM1, IM2i are advantageously generated in greyscale. Each image comprises a set of unit elements of type pixels or voxels are each characterized by an intensity I that can take a set of N values ​​(N being a finite integer greater than 1) corresponding to N shades of gray 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.

[0158] The TC processing and control unit is also advantageously configured to use the elementary images obtained to generate the map and / or to determine at least one data point relating to a representative area of ​​a cardiac lesion, as we will see in more detail later in the text.

[0159] The elementary images generated by the TC processing unit can be displayed on a screen of the INT human-machine interface. Recalibration

[0160] Advantageously, the process is devoid of an image registration step.

[0161] Alternatively, the image generation step (GEN) includes: registering black blood elementary images with each other and / or registering white blood elementary images with each other and / or registering black and white blood elementary images with each other. This makes it possible, particularly when the patient is breathing freely during the SE acquisition sequence, to limit the effects of respiration on the position of the heart and thus avoid spatial shifts induced by respiration in the images, which could affect the accuracy and reliability of analyses of these images or combinations of these images. Indeed, the respiratory rate is a priori different from the heart rate, but even if correlations exist between these two rhythms, it is possible for respiration to accelerate while the heart rate remains stable, or vice versa.

[0162] Advantageously, the process includes: registering elementary images in black blood of the same section.

[0163] Advantageously, the registration is carried out using an implementation of a non-rigid image registration algorithm.

[0164] Advantageously, the process includes the registration of elementary images in white blood of the same section.

[0165] Advantageously, the registration is carried out using an implementation of a non-rigid image registration algorithm.

[0166] Advantageously, the process comprises: registering elementary images in black blood IM1 and in white blood IM2i with each other.

[0167] Advantageously, this registration is carried out using an implementation of a non-rigid image registration algorithm.

[0168] Advantageously, the process includes: registering elementary images in white blood and elementary images in black blood of the same section.

[0169] Advantageously, this registration is carried out using an implementation of a non-rigid image registration algorithm.

[0170] Advantageously, these algorithms are identical. It is possible to choose a different algorithm for processing the elementary images in black and white blood, but it is preferable to choose the same algorithm for ease of implementation. Registration significantly improves the quality, particularly the contrast, of an image derived from a plurality of elementary images of the same slice. Furthermore, it reduces artifacts related to respiration.

[0171] According to a first example, at least one of the non-rigid algorithms is based on the mutual information method between images, founded on statistical relationships. The function to be optimized can be implemented using a statistical similarity criterion. One advantage of this method is that the matching of homologous attributes of images of the same cross-section is independent of their geometric position. Furthermore, this method is particularly effective for registering elementary images with different contrasts, such as images of black blood and white blood.

[0172] According to a second example consistent with the first example, at least one of the non-rigid algorithms is based on a transformation model. The transformation model allows the determination of functions for minimizing the difference between two images. The difference can be expressed as a geometric error to be minimized. Different approaches can be used, such as those based on extracting geometric primitives or shape descriptors, such as salient points, shape singularities, or contours, from each of the images. A parametric or non-parametric approach can be used.

[0173] According to an example of optimization of a transformation model or a similarity criterion, the least squares method can be used.

[0174] Other optimization methods can be implemented, such as gradient descent. However, this latter method is more specifically applied to image intensities and is not optimal within the scope of the invention, since the aim is to optimize the sharpness and contrast of the merged image. Nevertheless, the invention includes this embodiment.

[0175] Registration can be performed by choosing a reference image and determining a transformation function for 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 under consideration.

[0176] During the acquisition of three-dimensional images, it is possible to acquire several three-dimensional images of the heart, which can possibly be recalibrated. Mapping relaxation times

[0177] Advantageously, the TRA processing step includes a step in which a CA map of the relaxation times of the area to be imaged is generated from the white blood signals acquired during the respective CBi pairs of cardiac cycles, with i = 1 to N of the SE acquisition sequence.

[0178] Advantageously, for this purpose we use the white blood images IM2i with i = 1 to N.

[0179] More specifically, the CTO generation step of the CA time mapping of Relaxation includes: - determine relaxation times associated with the tissues of the area to be imaged.

[0180] In the case of a relaxometry in Tlrho, the relaxation time is the Tlrho.

[0181] In the case of T2 relaxometry, the relaxation time is T2.

[0182] For this purpose, the relaxation time associated with at least one pixel or voxel of a white blood image IM2i is advantageously determined from the signals acquired for this pixel or voxel during the different elementary acquisition steps Sei with i= 1 to N of the acquisition sequence SE.

[0183] Advantageously, for this purpose, the intensities of this pixel or voxel are used in the white blood images IM2i with i= 1 to N, whether registered or not.

[0184] It is considered that a pixel or voxel occupying the same position in the white blood images IM2i with i = 1 to N comes from the same area (pixel or voxel) of the area to be imaged.

[0185] More specifically, for the pixel or voxel, a relaxation curve is determined associated with the signals acquired for this pixel or voxel during the relaxometry sequence.

[0186] The relaxation curve is defined by the following relationship:

[0187] SI(TCARACT) = MQeTCARACT / r^

[0188] Where MO is the equilibrium magnetization (unitless) and where TCARACT is the characteristic time in milliseconds. This time is the spin-lock time or TSL for a Tlrho relaxometry sequence and the T2 preparation modulus time for a T2 relaxometry sequence.

[0189] TRELAX is the relaxation time associated with the sequence in milliseconds. It is Tlrho for a Tlrho relaxometry sequence and T2 for a T2 relaxometry sequence.

[0190] SI(TCARACT) is the intensity of the pixel or voxel measured for the characteristic duration TCARACT.

[0191] This step includes, for example, for each pixel or voxel, the determination of the relaxation curve which, among a set of relaxation curves, is closest, in the sense of a predetermined criterion, to the intensities (or signals) associated with that pixel or voxel for the different characteristic durations.

[0192] The relaxation time associated with this decay curve is the tissue relaxation time associated with the pixel.

[0193] This step is, for example, implemented by linear regression, for example by a least squares method or by a Levenberg-Marquardt type optimization process.

[0194] Alternatively, the curve is obtained by a dictionary matching method called "dictionary matching" in Anglo-Saxon terminology.

[0195] This step includes, for example, for a pixel or voxel, determining the relaxation curve that, among a set of relaxation curves obtained by simulation, is closest, according to a predetermined criterion, to the intensities (or signals) associated with that pixel or voxel for the different characteristic durations. This set of relaxation curves is obtained by Block simulation or by using an EPG (Extended Phase Graph) framework. These tools make it possible to simulate the proposed relaxometry sequence for a large set of Tlrho or T2 values. The simulated signals are compared to the acquired signals. The Tlrho (or T2) value corresponding to the curve closest to the MRI signals is retained. This process is performed for all pixels in the image.

[0196] The CTO generation step of the CA mapping makes it possible to obtain a map of relaxation times for all pixels or voxels of the area to be imaged, i.e. of a white blood image.

[0197] In other words, this step consists of generating, for each pixel or voxel in the area to be imaged, a pixel or voxel / relaxation time pair. In other words, a relaxation time is associated with each pixel or voxel in the area to be imaged.

[0198] Alternatively, the mapping includes the relaxation times of all or part of the pixels or voxels of the IM2i white blood image.

[0199] The TC processing and control unit is advantageously configured so as to generate, during a GENR generation step, at least one representation of a data determined during the general processing step TRAG or during the TRA processing step.

[0200] Advantageously, the TC processing and control unit is configured so as to control the display, during an AFFD display step, of at least one representation or image generated by the TC processing and control unit, either automatically or when a user transmits a command to the processing unit via an INTE input interface of the INT human-machine interface.

[0201] The method advantageously includes an AFFD display step in which at least one visual representation of the CA mapping is displayed on a screen of an INTS output interface of the system.

[0202] One advantage is to give a specialist an indicator of a diffuse change in the tissues of the area to be imaged, in particular the myocardium.

[0203] For example, a color image can be displayed where the color of each pixel or voxel is defined by its relaxation time. This effect is associated with distinct colors for distinct and disjoint relaxation time ranges.

[0204] Alternatively or in addition, a visual representation of the map can be displayed in the form of a bullseye-type representation, which will be described later. Image combination

[0205] Since 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.

[0206] Advantageously, the GEN image generation step includes: combining the white blood images of the IM2i section with i= 1 to N so as to obtain a combined ISBen white blood image of the section.

[0207] Advantageously, the method comprises: combining the black blood images IMli, for example for i = 1 to N of the section so as to obtain a combined black blood image ISN of the section.

[0208] This helps to reduce noise and increase the signal-to-noise ratio or SNR (acronym for the Anglo-Saxon expression "signal-to-noise ratio").

[0209] Alternatively, M black blood images IM1 are combined so as to obtain the combined black blood image (where M is an integer less than N and greater than 1) taken from among the N black blood images generated for the cut and / or P white blood images IM2i are combined ((where P is an integer less than N and greater than 1) taken from among the N white blood images generated for the cut so as to obtain the combined white blood image.

[0210] Image combination can be performed before, during or after image generation GEN.

[0211] According to one example, the combination is an averaging. Averaging is, for example, performed in image space or in Fourier space (i.e., the frequency domain, before image reconstruction). These solutions are computationally inexpensive and fast.

[0212] Averaging has the advantage of preserving image detail, since it increases the signal-to-noise ratio (SNR). This technique smooths noise to reduce residual image artifacts. Furthermore, averaging improves the bit depth of the digital image beyond what is possible with a single image.

[0213] One advantage of the step of averaging images taken from the same slice is to reduce noise. The noise amplitude decreases as the square root of the number of images used; that is, with only 4 images, the amplitude can be reduced noise by a factor of two. According to an example of a free breathing acquisition lasting 2 minutes, it is possible to collect 4 to 5 images per slice plane, which allows for good noise reduction performance.

[0214] In one embodiment, image combination can alternatively be implemented by motion-compensated iterative reconstruction. In other words, this type of combination is implemented during image reconstruction. Compensated MRI reconstruction techniques are described in particular in the following articles: Odille F, et al., “Generalized reconstruction by inversion of coupled 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.

[0215] Advantageously, the elementary images on the one hand in black blood and on the other hand in white blood are respectively combined so as to produce a combined black blood image ISN per slice and a combined white blood image ISB per slice.

[0216] During the acquisition of 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.

[0217] Determination of data relating to a representative area of ​​a cardiac lesion

[0218] Advantageously, the TRA processing step comprises: - determine DET at least one data relating to at least one ZI zone representative of a cardiac lesion, or cardiac lesion zone, from a black blood reference image from at least one of the elementary black blood images IMli with i = 1 to N generated from the black blood signals acquired during the SE acquisition sequence, from at least one of the white blood images IM2i with i = 1 to N generated from the black blood signals acquired during the SE acquisition sequence.

[0219] One advantage is to determine from the same acquisition sequence not only a map of relaxation times but also data relating to an area representative of a cardiac lesion identified on a black blood image, enriched using the anatomical characteristics of the white blood image.

[0220] Advantageously, the location data of the ZI zone representative of a cardiac lesion or an indicator of the location of the ZI zone representative of a cardiac lesion.

[0221] The ZI zone representative of a cardiac lesion is advantageously obtained by segmentation of the reference image in black blood as we shall see later.

[0222] According to one embodiment, the ZI zone representative of a cardiac lesion is a zone whose pixels have an intensity greater than a predetermined intensity threshold on the reference image in black blood.

[0223] The threshold can be defined prior to the process or be defined according to the range of intensities of the reference image in black blood.

[0224] The threshold can be global, that is to say the same for all pixels or voxels of the reference image in black blood.

[0225] Alternatively, the threshold is local. In other words, it is defined and can vary according to the pixels or voxels of the reference image in black blood.

[0226] According to one embodiment, the black blood reference image is a combined black blood image ISN obtained by combining the N black blood images IMli with i = 1 to N. One advantage is to take advantage of the benefits of combining black blood images and in particular the limitation of the SNR.

[0227] Advantageously, this combination is an average.

[0228] Alternatively, this combination is of another type described previously.

[0229] Alternatively, the reference image in black blood is one of the black blood images IMli with i between 1 and N or a combined image obtained by combining M black blood images taken from the N black blood images IMli with i = 1 to N.

[0230] In a particular embodiment, the data is determined from the N white blood images IM2i, for i = 1 to N.

[0231] For example, the data is determined from a white blood reference image from the N white blood images for i = 1 to N.

[0232] The inventors have found that the combined white blood image ISB, in particular the average, provides substantially the same anatomical information on the myocardium as a combined white blood image, for example an average, obtained from N white blood images generated from signals acquired by implementing identical elementary acquisition sequences.

[0233] Alternatively, the data is determined from only one of the white blood images IM2i, for a given index i.

[0234] One advantage is avoiding the impact of variations in the characteristic duration (HSL or echo duration) on the quality of the generated data. Indeed, varying the characteristic duration slightly alters the contrast of white blood images.

[0235] Advantageously, when the relaxometry sequence is a Tlrho relaxometry sequence, a white blood image is generated from the white blood signals acquired for a TSL spin-lock duration greater than or equal to 20 ms.

[0236] For example, the white blood image generated from the acquired signals is used by implementing the elementary acquisition sequence Sei with the longest spin-lock time TSL or the T2 preparatory module with the longest duration among the N elementary acquisition sequences Sei with i = 1 to N. This white blood image is the one that presents the highest contrast between the blood and the myocardium, which allows obtaining a maximum of anatomical information on the myocardium.

[0237] Advantageously, when the relaxometry sequence is a Tlrho or Tlp relaxometry sequence, a white blood image is used, generated from white blood signals acquired for a TSL spin-lock duration greater than or equal to 20 ms.

[0238] According to another embodiment, the data is determined from G white blood images IM2i taken from the N white blood images where G is an integer greater than 1 and less than N.

[0239] For example, we use the G white blood IM2i images generated from the white blood signals acquired for the largest TSLi spin lock durations among the N TSLi spin lock durations with i = 1 to N or from the white blood signals acquired for the largest T2 preparation modulus durations among the N T2 preparation modulus durations with i = 1 to N.

[0240] Determination of the area representative of a cardiac lesion

[0241] In a particular embodiment of the invention, each ACQ2 white blood acquisition step is distinct from a reversal-recovery sequence.

[0242] In other words, this sequence is devoid of a pulse reversing the longitudinal magnetization of the area to be imaged.

[0243] Consequently, and unlike PSIR imaging, during white blood acquisition, the longitudinal magnetization of the myocardium is not canceled, which allows for images with a higher contrast between the myocardium and the blood, and thus facilitates image processing.

[0244] Alternatively, each white blood acquisition step is a phase-sensitive inversion recovery sequence. This is, for example, a PSIR sequence (from the English expression "phase-sensitive inversion-recovery").

[0245] Advantageously, the white blood acquisition is configured so that, during the implementation of the LE2 readout module, the respective longitudinal magnetizations of the healthy myocardium, blood, and lesions are positive, and the longitudinal magnetization of the lesions is between the magnetization of the healthy myocardium and that of the blood. This is achieved by the configuration of the PREP2i preparatory module and that of the LE2 readout module, and by the relative temporal positioning between these two modules.

[0246] As can be seen in [Fig. 5], the DET determination step of the data relating to a ZI zone representative of a cardiac lesion advantageously comprises the following steps: - a first SEG1 segmentation, by computer, of at least one white blood reference image derived from at least one of the elementary white blood images IM2i with i = 1 to N so as to generate positioning data for a first L1 wall delimiting the myocardium, - second segmentation SEG2, by computer, of the reference image in black blood using positioning data of the first wall L1 taken from the data calculated during the SEG1 segmentation step, so as to determine the ZI zone representative of a cardiac lesion.

[0247] These steps are implemented by the TC processing and control unit.

[0248] In the remainder of the text, it is considered, as in the example in [Fig. 5], that The reference image in white blood ISB is a combined image in white blood ISB and the reference image in black blood ISN is a combined image in black blood ISN.

[0249] One advantage is to allow automatic, reproducible, reliable and precise segmentation of a ZI zone representative of a cardiac lesion.

[0250] Indeed, the segmentation of ISB white blood images generated from signals measured during ACQ2i white blood acquisition steps, particularly when they are distinct from inversion-recovery sequences, allows for automatic, robust, reliable, and precise positioning of the walls delimiting the myocardium, because these images exhibit significant contrast between the myocardium and the blood. Black blood images do not allow for such good results due to the lack of contrast between healthy myocardium and blood.

[0251] Segmenting the reference image in black blood to determine, i.e. identify, extract or delineate, the representative area of ​​a cardiac lesion makes it possible to obtain good results that would not be possible to obtain on its own with the image in white blood containing little or no information on the lesions. First segmentation

[0252] According to one example, the first segmentation SEG1 is implemented using at least one ISB white blood reference image 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.

[0253] Positioning data relating to a wall corresponds, for example, to the identification of the pixels constituting the wall.

[0254] The result of this segmentation is visible in [Fig. 6], schematically representing, at the top left, a white blood image of a section of the ISB heart and at the top right the white blood image of the section on which the first wall L1 and the second wall L2 obtained during the first segmentation step SEG1 are represented in thick black lines.

[0255] The generated images are, for example, in greyscale. The white areas in [Fig. 6] represent areas lighter than the dotted areas, which represent areas lighter than the gridded areas, which represent areas lighter than the bricks.

[0256] In the example of [Fig.6], the heart cavity is the left ventricle and the first segmentation SEG1 is implemented so as to delimit the walls L1, L2 of the part of the myocardium surrounding and delimiting the left ventricle.

[0257] 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.

[0258] 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, that is, facing outwards from the left ventricle (LV), of the part of the myocardium surrounding the left ventricle (LV). This is the epicardium.

[0259] The second wall L2 is the wall of the myocardium delimiting the left ventricle. This is the endocardium.

[0260] On 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.

[0261] It is easy to understand that in 3D these walls form surfaces.

[0262] Alternatively, the first SEG1 segmentation is implemented so as to generate positioning data for only one of these two walls, for example the outer wall of the myocardium.

[0263] The first SEG1 segmentation is carried out by implementing a learning function or algorithm, for example an artificial neural network, to segment a white blood reference image so as to delimit at least one wall of the myocardium surrounding and delimiting a cavity of the heart.

[0264] In a non-limiting example, the learning function is a neural network.

[0265] The artificial neural network used for segmentation is advantageously a convolutional neural network.

[0266] The convolutional neural network is, for example, of the U-Net type.

[0267] Alternatively, the artificial neural network is of the transformer type, also called a self-attentive model, for example, of the type commonly called a "swin transformer" in Anglo-Saxon terminology.

[0268] The neural network, or more generally the learning function, is implemented on two-dimensional (2D) and / or three-dimensional (3D) images. In other words, it is trained to perform the desired segmentation by receiving 2D and / or 3D images as input.

[0269] Advantageously, the learning function is a trained learning function.

[0270] Advantageously, the learning function is trained, prior to the implementation of the method according to the invention or of the acquisition or processing step, from white blood images of the heart and more precisely of the area to be imaged, generated from signals acquired during respective white blood acquisition steps, and labeled by specialists, i.e. segmented by specialists, so that the trained learning function receiving input data including a white blood reference image of the heart and more precisely of the area to be imaged, is able to segment it so as to delimit at least one wall of the myocardium surrounding and delimiting a cavity of the heart.

[0271] When the ACQ2i white blood acquisition sequences are distinct from the inversion recovery sequences, the learning function is performed from white blood images of the heart generated by implementing acquisition sequences distinct from the inversion recovery sequences.

[0272] When the white blood reference image is a combined ISB image, the white blood images used for training are also combined images obtained from signals acquired during a relaxometry sequence.

[0273] Preferably, this relaxometry sequence is identical to the acquisition sequence consisting of the ACQ2i white blood acquisition sequences with i = 1 to N.

[0274] In a particular embodiment, the learning function is trained, prior to the implementation of the method according to the invention, from several sets of white blood training images of the area to be imaged generated from training signals acquired during respective acquisition steps by late gadolinium enhancement white blood magnetic resonance.

[0275] The images of the image sets are labeled by specialists, i.e. segmented by specialists, so that the trained learning function receiving input data including the white blood images IM2i with i= 1 to N, is able to segment them in such a way as to generate positioning data of at least one wall delimiting the myocardium.

[0276] Each training image set is obtained from white blood signals acquired by implementing a relaxometry sequence.

[0277] Preferably, this relaxometry sequence is identical to the acquisition sequence consisting of the ACQ2i white blood acquisition sequences with i = 1 to N.

[0278] In this particular embodiment, the learning function is configured to generate positioning data from input data comprising the N white blood IM2i images generated from the white blood signals acquired during the SE acquisition sequence.

[0279] The GENR generation step may include a step of generating a white blood output image corresponding to a white blood image, for example a reference image, in which the intensities of the pixels or voxels corresponding to the walls or contours L1 and L2 are replaced by predetermined intensities or colors enabling them to be differentiated from other tissues in the image.

[0280] The method may include a step of displaying this output image. Propagation

[0281] The method may include a step of propagating the walls detected during the first segmentation step SEG1, on the black blood reference image ISN and / or on the CA map.

[0282] In other words, the process may include a REP transfer step, i.e. propagation, comprising the identification, on the ISN black blood reference image or on the map, of the pixels or voxels corresponding to the walls L1 and L2 identified during the first SEG1 segmentation.

[0283] In [Fig.6], a black blood ISN image of the heart section is schematically represented at the bottom left and at the bottom right the black blood image on which the walls L1 and L2 detected during the first segmentation step SEG1 are represented in thick black lines.

[0284] The identification, on the black blood image ISN or on the CA map, 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 white blood reference image ISB.

[0285] These pixels or voxels can have the same respective positions on the white blood image and on the black blood image or the map when these images are considered to be spatially registered and to have the same size and resolution.

[0286] A predetermined or calculated spatial offset may alternatively be applied to these pixels or voxels when it is estimated that a spatial offset exists between these images.

[0287] The report may include the annotation or colouring of the pixels or voxels corresponding to the walls L1 and L2. Second segmentation

[0288] The second segmentation is implemented by the CT processing unit.

[0289] The DET determination step of the data relating to the ZI zone representative of a cardiac lesion may include a DE detection step of this ZI zone representative of a cardiac lesion.

[0290] Advantageously, when a ZI zone representative of a cardiac lesion is detected, the second SEG2 segmentation is implemented. In other words, the second SEG2 segmentation can only be implemented if a ZI zone representative of a cardiac lesion is detected during the DE detection step.

[0291] Alternatively, the DE detection step is implemented after the second segmentation step SEG2.

[0292] Alternatively, the DET determination step is devoid of a DE detection step.

[0293] The detection step will be described later.

[0294] The second segmentation SEG2 uses a black blood ISN reference image and positioning data of the first wall L1 and possibly those of the second wall L2 from the first segmentation SEG1.

[0295] This positioning data can be positioning data generated during the first segmentation step SEG1 or positioning data from the REP reporting step.

[0296] Alternatively, the second segmentation step SEG2 includes the carryover step.

[0297] The second SEG2 segmentation allows one or more representative areas of cardiac lesions to be located, i.e. to generate localization data for these areas.

[0298] These location data include, for example, the identification or positions of pixels or voxels corresponding to these areas representative of cardiac lesions.

[0299] In other words, the second SEG2 segmentation allows the extraction, that is to say the delineation, of the representative areas of cardiac lesions on the reference image in black blood.

[0300] The second segmentation SEG2 is, for example, implemented by thresholding.

[0301] It advantageously includes the identification of pixels or voxels having an intensity greater than or equal to the predetermined intensity threshold only in a predetermined search area Z of the black blood reference image ISN delimited by the first wall L1 and / or the second wall L2.

[0302] Indeed, as can be deduced from [Fig.2], the representative areas of cardiac lesions exhibit, on the black blood images, a high intensity compared to the healthy myocardium and blood.

[0303] This search area Z is determined from the positioning data of the first wall L1 and possibly those of the second wall L2 from the SEG segmentation.

[0304] This refers, for example, to the area of ​​the reference image in black blood ISN delimited by the pixels or voxels of the first wall L1 transferred to the black blood ISN image and / or the pixels or voxels of the second wall L2 transferred to the black blood ISN image.

[0305] Advantageously, the search area for the black blood image is the area surrounded and delimited by the first wall Ll.

[0306] In other words, the second segmentation step SEG2 includes searching for pixels or voxels with an intensity greater than or equal to a predetermined intensity threshold only within the search area delimited and surrounded by the first wall L1 on the reference black-blood image ISN. That is to say, these pixels or voxels are selected only from among the pixels or voxels of an area of ​​the reference black-blood image surrounded and delimited by the first wall L1. This avoids detecting areas representative of lesions beyond the epicardium.

[0307] Alternatively, the search area Z is the area of ​​the black blood image(s) ISN delimited by the first wall L1 and by the second wall L2.

[0308] In other words, the second segmentation SEG2 includes the search 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 the ISN black blood reference image. This variant has the advantage of identifying only myocardial pixels or voxels.

[0309] Alternatively and / or in addition, the second segmentation SEG2 includes the search for pixels of intensity greater than or equal to the predetermined intensity threshold only in the area surrounded by the L2 wall. This step makes it possible to identify only the pixels or voxels of papillary muscles.

[0310] Alternatively, the second segmentation is implemented using a neural network, for example, a convolutional neural network trained to segment representative areas of cardiac lesions with predetermined characteristics 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 reference image in black blood, or using at least one active contour segmentation algorithm, i.e. a segmentation algorithm using an active contour model.

[0311] Alternatively, the DET determination step is devoid of the first segmentation step.

[0312] The determination step includes a segmentation step of the reference image in black blood so as to determine the ZI zone representative of a cardiac lesion.

[0313] This step is, for example, carried out by thresholding or by using a neural network as described above.

[0314] The DE detection step includes detecting the absence or presence of an area representative of a cardiac lesion using an ISN blood-black image and positioning data of at least one wall, for example, the second wall L2.

[0315] This step generates an output indication of the presence or absence of an area representative of a cardiac lesion.

[0316] The DE detection step can be performed by thresholding or by using a neural network as in the segmentation step. This step can consist of determining whether a number of contiguous pixels or voxels exceeding a predetermined threshold exhibits an intensity exceeding a predetermined threshold in the area delimited by the L1 and / or L2 walls, the positioning of which is defined during the first segmentation step SEG1 of the myocardium. A region representative of a cardiac lesion is detected if this condition is met, and the absence of a region representative of a cardiac lesion is detected if this condition is not met.

[0317] The neural network is, for example, a convolutional neural network.

[0318] The neural network is, for example, trained to detect the presence or the absence of a representative area of ​​cardiac lesion 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 reference image in black blood.

[0319] In [Fig.7], the reference image in black blood ISN is shown, on which The L1 and L2 limits identified during segmentation and reported and delimiting, for example, the Z search area, i.e. propagated, on the black blood ISN image, as well as the pixels identified as being pixels of the ZI area representative of a cardiac lesion, have been represented in thick lines.

[0320] The DET determination processing step may include at least one of the following steps: - Calculation of the TA size CTA of the ZI zone representative of a cardiac lesion from the positioning data of the first wall L1 and possibly the second wall L2 from the first segmentation step SEG1, - CTT calculation of at least one degree of TR transmurality of the ZI zone representative of a cardiac lesion from the first wall L1 and the second wall L2 resulting from the first segmentation step SEG1.

[0321] These calculations are performed using a set of at least one black blood image.

[0322] By size of a ZI zone representative of a cardiac lesion, we mean a data representative of the dimensions of the zone, such as a volume or a surface, for example or a number of pixels or voxels. Size calculation

[0323] The DET determination advantageously includes a CTA calculation step of the size of the ZI zone representative of a cardiac lesion.

[0324] This CTA calculation step includes the determination of at least one elementary data representative of the size of at least one area representative of a 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 from the first segmentation SEG1 of the myocardium or data from these data, for example, data from the transfer step or positioning data of the ZI area representative of a cardiac lesion obtained during the second segmentation step SEG2.

[0325] An elementary data point representative of the size of a ZI zone can be a percentage of a myocardial surface occupied by the ZI zone on a SEC sector of the ISN black blood reference image or a volume or mass of the ZI zone representative of a cardiac lesion in this SEC sector originating from axis 1 parallel to the p axis and passing substantially through the center of the cardiac cavity on the ISN black blood image and delimited by two rays R originating from axis 1 as seen on the ISN black blood image.

[0326] The percentage of the myocardial surface occupied by the ZI zone representing a cardiac lesion in the SEC sector can be calculated from the ratio between the number of pixels corresponding to the ZI zone representing a cardiac lesion in this SEC sector and the number of pixels corresponding to the myocardium in this SEC sector.

[0327] The number of pixels corresponding to the ZI zone representing a cardiac lesion in this SEC sector can be calculated from the localization data obtained during the second segmentation step SEG2 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

[0328] The CTA step may include calculating a representative data point for the size of the ZI zone representative of a cardiac lesion in a SEC sector from several elementary data points representative of the size of the ZI zone representative of a calculated cardiac lesions, in this SEC sector, for several black blood ISN images distributed along the p axis.

[0329] For example, a combination or average of the elementary data is calculated.

[0330] The volume of the ZI zone representative of a cardiac lesion in a sector SEC can be calculated from the ratio between the number of pixels corresponding to the ZI zone representative of a cardiac lesion on this sector and the number of pixels corresponding to the myocardium on this sector, from the thickness of the slice corresponding to the reference image in black blood ISN, when the image is two-dimensional.

[0331] The size and / or volume are advantageously also calculated from the predetermined resolution of the images.

[0332] It should be noted that the density of the myocardium is 1.06 g / mL. It is therefore considered that the mass of a ZI zone representative of a cardiac lesion is substantially equal to the volume of the latter, which makes it possible to evaluate the mass of the ZI zone representative of a cardiac lesion. The CTA calculation step can, for example, include the division of the reference image in black blood ISN into a first predefined number, equal to 12 in the non-limiting example of Figure 1, predefined SEC sectors of the same angle "1" of opening pointing towards axis 1 and the calculation of the percentage of the myocardial surfaces occupied by the ZI zone representative of a cardiac lesion on the different SEC sectors.

[0333] The first number of sectors and the opening angle "1 can vary depending on the cutting plane PCk. For example, the closer the cutting plane PCk is to the apex along the major axis, the lower the number of sectors and the higher the opening angle.

[0334] The general processing step TRAG advantageously includes, a GENR generation step, by computer, by the processing unit, of a set of at least one representation of data relating to a ZI zone representative of a cardiac lesion and an AFFD display step of at least one representation of the set of at least one representation on a screen of the human-machine interface.

[0335] The GENR generation step includes, for example, the generation of data representative of the result of the DE detection step, i.e., the absence or presence of a ZI zone representative of a cardiac lesion, and the display step includes the display of this data on a screen of the human-machine interface.

[0336] The set of at least one representation advantageously includes a first REPT representation of the data representative of the ZI zone representative of a cardiac lesion calculated during the CTA calculation step associated with at least one TRA processing step.

[0337] For example, one can generate, as seen in [Fig.7], a bull's eye type representation of the percentages or data representative of the percentages of the myocardial surfaces occupied by the ZI zone representative of a cardiac lesion in different sectors of ISN black blood images taken according to the respective PC slice planes.

[0338] The Bull's eye representation is defined by the American Heart Association (AHA) in reference to the Anglo-Saxon expression "American Heart Association" and described in the following article: "Standardized Myocardial Segmentation and Nomenclature for Tomography 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.

[0339] The bullseye representation comprises a plurality of concentric circles CE separated in pairs by rings CO. Each ring CO is assigned to a slice or set of contiguous slices, such that the closer the CO ring corresponds to a slice or set of slices to the apex, the closer it is to the center of the circles. Each ring CO is divided into portions of sectors PSE in which are displayed, as in the example of [Fig. 7], the percentages of the myocardial surface occupied by a ZI zone representative of a cardiac lesion and calculated for the respective sectors of the ISN black blood image of the corresponding slice or combinations, for example, means, of percentages calculated from the percentages of the myocardial surface occupied by a ZI zone representative of a cardiac lesion calculated for the sectors of the black blood images of the corresponding set of slices.

[0340] Alternatively and / or in addition, the intensity of the pixels in the different portions of the crowns depends on the calculated percentage. The lower this percentage, the higher the intensity of the corresponding crown.

[0341] For example, in [Fig.7], the display was represented in the form of a known bullseye type representation of the respective means of the percentages of the size of the ZI zone representative of a cardiac 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), the mid-ventricle (middle crown) and the basal zone (outer crowns).

[0342] 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.

[0343] When generating a 3D image of the core, the image is advantageously divided into several layers along the p-axis and the same data is calculated from these different layers as from 2D images.

[0344] According to one embodiment, the generation step includes a step of generating a bullseye-type representation from the relaxation time mapping.

[0345] Maps calculated for different cross-sections are advantageously used.

[0346] This representation is generated from the CA relaxation time mapping(s) and L1 and L2 wall positioning data generated during the first SEG1 segmentation.

[0347] The PSE sector portions are then associated with respective relaxation time values ​​or with medians or standard deviations of relaxation time. Transmurality

[0348] The DET determination step advantageously includes a step for calculating a data representative of a percentage of transmurality of a ZI zone representative of a cardiac lesion. By percentage of transmurality, we mean the percentage of a myocardial thickness occupied by a ZI zone representative of a cardiac lesion.

[0349] This CTT calculation step includes determining at least one data point representative of the percentage of transmurality of at least one ZI zone representative of a cardiac lesion using lesion localization data and first and / or second wall localization data L1, L2 from the segmentation step.

[0350] This data may be a percentage of the myocardial thickness occupied by a ZI zone representative of a cardiac lesion on a sector of the image in black blood ISN starting from axis 1.

[0351] The percentage of myocardial thickness occupied by the lesion in the sector can be calculated from the ratio between the number of pixels corresponding to the thickness of the ZI zone representative of a cardiac lesion in that sector and the number of pixels corresponding to the myocardial thickness in that sector. The number of pixels corresponding to the thickness of the ZI zone representative of a cardiac lesion can be a number obtained from the results of the SC lesion segmentation step or be calculated, for example by thresholding, during the CTT calculation step from the L1 and possibly L2 positioning data from the SEG1 first segmentation step.

[0352] The number of pixels corresponding to the thickness of the ZI zone representing a cardiac lesion in a sector can be an average or a maximum of numbers of pixels, corresponding to the thickness of the ZI zone representative of a cardiac lesion, calculated according to different radii of the sector.

[0353] The number of pixels corresponding to the myocardial thickness in a sector can be an average or a maximum number of pixels, corresponding to the myocardial thickness, calculated according to different radii of the sector. These numbers are calculated from positioning data of the L1 and L2 walls.

[0354] The CTT calculation step may, for example, include dividing the ISN black blood image into a second predefined number of sectors of the same opening angle pointing towards the center of the cardiac cavity and calculating the percentage of the myocardial surfaces occupied by the lesion on the different sectors.

[0355] The second number of sectors, and therefore the opening angle, can vary depending on the PC cutting plane. For example, the closer the PC cutting plane is to the apex, the lower the number of sectors and the higher the opening angle.

[0356] Advantageously the second number is greater than the first number.

[0357] The GEN generation step may include calculating a representative transmurality data in a sector from several representative transmurality elementary data calculated in that sector for several ISN black blood images distributed along the p-axis.

[0358] For example, a combination or an average of the elementary data is calculated.

[0359] The GENR step advantageously includes the generation of a REPTR representation of data representative of a percentage of transmurality. The AFFD display step advantageously includes the display of this representation.

[0360] For example, one can generate a bullseye-type representation of combinations, for example of means, of percentages of the transmurality of the lesion in sectors of contiguous ISN black blood image sets taken according to the respective PC cutting planes.

[0361] The intensity of the pixels in this image advantageously, but not necessarily, represents the percentage of transmurality.

[0362] For example, in [Fig.7], the display was shown in the form of a bullseye type REPTR representation of the average percentage transmurality of the ZI zone representative of a cardiac lesion calculated in the sectors defined on several sets of contiguous black blood images taken according to respective section planes distributed along the p-axis.

[0363] Just as before, the bullseye type representation includes a plurality of sector portions whose intensity corresponds to the combination of the percentage of transmurality calculated for that sector portion.

[0364] 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. Image merged

[0365] In a particular embodiment, the DET determination step includes the generation of a fused image by replacing pixel or voxel values ​​of a first white blood reference image from white blood signals acquired during at least one of the pairs with new values ​​enabling the representative area of ​​a cardiac lesion to be distinguished from other tissues in the area to be imaged.

[0366] This step may be one of the steps in the generation step.

[0367] This merged image constitutes a location indicator for the industrial zone representative of a cardiac lesion.

[0368] The new pixel values ​​are, for example, colors.

[0369] The method advantageously includes a step of displaying this merged image.

[0370] One advantage is that it allows a specialist to easily locate the area representative of a cardiac lesion in relation to the anatomy of the myocardium. Advantages

[0371] The proposed solution makes it possible to obtain images with sufficient resolution and contrast to detect and determine data relating to representative areas of cardiac lesions in a reliable and reproducible manner.

[0372] Furthermore, by separating two important pieces of information—namely, the anatomy of the heart and the representative areas of lesions—onto two distinct images, namely white blood images and black blood images respectively, it enables the implementation of an automated process for characterizing the representative areas of lesions. This automation allows for a significant gain in time and reproducibility compared to prior art solutions.

[0373] The black and white blood acquisition sequence of the method requires a relatively short acquisition time, particularly when acquiring signals to generate 2D images that involve little computation. This advantageously allows the acquisition sequence to be performed during breath-holding and limits cardiac movement between images, thus reducing the corrections required. This, in turn, limits computational resources and enables real-time implementation of the method. It also limits artifacts that impair image readability. These artifacts increase the difficulty of reconstructing clear and precise images to locate and detect the representative lesion area. Furthermore, long MRI acquisitions are uncomfortable for the patient. A duration of 10 to 20 minutes is considered a very long duration and it is difficult for the patient to remain inside the MRI without making any movement.

[0374] Furthermore, in the case of 2D image generation, artifacts are avoided in an image extracted from a cross-section of the 3D image, which could lead to situations where it is impossible to distinguish the presence of a representative lesion area from the presence of blood located near the muscle. Indeed, in some cases, the lesion is so close to the blood, referred to as subendocardial, that it is difficult to determine, in images exhibiting artifacts, whether it is a representative lesion area, blood, or an image artifact.

[0375] Furthermore, the proposed solution makes it possible to generate a relaxation time map enabling the quantification of diffuse tissue changes, which could allow a specialist to identify cardiomyopathies (e.g., diffuse and acute). The proposed solution does not require the addition of a specific acquisition sequence for this purpose, thus avoiding numerous drawbacks such as prolonged examination, additional breath-holding for the patient, extra effort for the radiographer, and complex analysis because the images of the two sequences are not spatially registered. Material

[0376] From a hardware point of view, the TC processing and control unit can be seen as a computer interacting with computer programs.

[0377] The processing and control unit TC includes, for example, a computer, comprising a set of at least one processor, and optionally a memory operationally coupled to the computer.

[0378] The memory includes, 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 a communication unit.

[0379] In other words, the computer-readable medium is a tangible medium. That is to say, it is not a transient signal in itself, 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.

[0380] By way of example, the readable medium is an optical disc, a magneto-optical disc, a read-only memory (ROM), an erasable and programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM, from the English Electrically Erasable Programmable Read-Only Memory), random access memory (RAM, from the English Random Access Memory), a magnetic card or an optical card.

[0381] The readable medium may include an operating system and load the programs according to the invention. It includes registers adapted to store parameter variables created and modified during the execution of the aforementioned programs. A computer program containing software instructions is then stored on the readable medium.

[0382] Alternatively, the program instructions are taken from an external source and downloaded via a network. This is particularly the case for applications.

[0383] The processing and control unit TC includes a computer, that is to say at least an 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 register memories or other types of display devices, transmission devices or storage devices.

[0384] The CT processing and control unit includes, for example, one or more memories, for storing data, and operationally coupled to the data processing circuit and a reader adapted to read a computer-readable medium.

[0385] The memory or memories are advantageously provided for storing acquired signals and / or data generated during processing, for example, black-blood images and / or white-blood images and / or at least one relaxation time map and / or data relating to the ZI zone and / or one or more representations and / or at least one other image generated during processing, for example a fused image.

[0386] The processing and control unit TC includes, for example, at least one computer, for example, a microcomputer, a network of computers, an electronic component, a tablet, a smartphone or a personal digital assistant (PDA).

[0387] The steps of the method according to the invention are, for example, carried out by causing the processing circuits of the CT processing and control unit to read predetermined programs stored on materials such as memories so that the data processing circuits perform calculations, control communications and / or elements of the imaging system and / or read and / or write data into memories.

[0388] The processing or global processing step is, for example, carried out on a processing device, for example a single computer, or on a system distributed among several computers (in particular via the use of cloud computing).

[0389] The CT processing and control unit comprises at least one computer comprising at least the following elements: 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 a microcontroller and / or a digital signal processor (DSP)) ASICs capable of interpreting instructions in the form of a computer program and / or a hardware set such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a programmable logic device (PLD), programmable logic arrays (PLAs), a system-on-chip (SOC), and / or an electronic board in which steps of the method according to the invention are implemented in hardware elements.

[0390] 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 process according to the invention.

[0391] The product-program may include the computer-readable recording medium.

[0392] The invention also relates to a computer-readable medium on which the computer program is recorded.

[0393] Alternatively, the program instructions are obtained from an external source and downloaded via a network. This is particularly the case for applications. In this case, the computer program product includes a computer-readable data carrier on which the program instructions are stored or a data carrier signal on which the program instructions are encoded.

[0394] The form of program instructions is, for example, a form of source code, a computer-executable form, or any intermediate form between source code and a computer-executable form, such as the form resulting from the conversion of the source code via an interpreter, assembler, compiler, linker, or locator. Alternatively, program instructions are microcode, firmware instructions, state definition data, integrated circuit configuration data (e.g., VHDL), or object code. 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).

[0395] The communication unit comprises at least one communication device enabling communication between the elements of the system and optionally between at least one element of the system and a device external to the system. The systems communication systems can establish a physical link between system elements and / or between a system element and a device outside the system and / or a remote (wireless) communication link between system elements and / or between a system element and a device outside the system.

[0396] The communication device may include 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. To enable data communication between different devices, which may include communication devices, these devices include hardware, firmware, and / or software for establishing a wired or wireless communication link between them, for example, Wi-Fi, Bluetooth, cellular, or Ethernet.

[0397] The INT user interface allows a user to enter data or commands in order to be able to interact with the programs according to the invention.

[0398] The INT user interface includes, for example, an INTS output interface and an INTE input interface.

[0399] 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 speech recognition device, a haptic device.

[0400] The INTS output interface is designed to provide information to a user, either sensorially or electrically, such as, for example, visually or audibly. The output interface includes, for example, a display or screen. The AFFD display stage may be a step for providing information by means other than a display or screen.

[0401] The output interface INTS can be the input interface INTE, for example, in the case of a touch tablet.

[0402] Advantageously, the S or C system is configured so that a user can select, by transmitting a command to the TC processing and control unit via the INTS input interface, one or more representations or images generated during the TRAG or TRA processing step so that each selected image or representation is displayed on one or more screens of the INTS output interface during an AFFD display step.

[0403] For example, a user can select, via the input interface, one or more representations or images generated for a predetermined section.

[0404] Advantageously, a user can select, via the input interface, a predetermined slice taken from a plurality of slices so that one or more images and / or one or more representations generated during the TRAG processing step are automatically displayed on one or more output interface screens or so that the user can select one or more representations and / or one or more generated images for a predetermined cut.

[0405] Alternatively, the processing and control unit is configured to automatically display only one or more images and / or one or more representations generated during the TRAG processing step, for a predetermined section, during the display step.

Claims

Demands

1.

2. Cardiac magnetic resonance imaging procedure, the procedure including an acquisition sequence (ES) comprising: acquire signals comprising, during each pair, consisting of two consecutive cardiac cycles, successive pairs: to acquire (ACQ1) dark blood signals from an area to be imaged in an individual's heart by dark blood magnetic resonance imaging with late gadolinium enhancement, the dark blood signals allowing the generation of an elementary dark blood image of an area to be imaged, to acquire (ACQ2i) white blood signals from the area to be imaged by white blood magnetic resonance imaging with late gadolinium enhancement, the dark blood signals allowing the generation of an elementary dark blood image of the area to be imaged, The white blood signals acquired during successive pairs are acquired by implementing a relaxometry sequence, the procedure comprising: to process, by computer, signals acquired during successive cardiac cycles, including: Generate (CTO) a tissue relaxation time map (CA) of the area to be imaged from the white blood signals acquired during the pairs successive cardiac cycles. generate black blood images (IM1) from black blood signals acquired during successive pairs, generate white blood images (IM2i) from white blood signals acquired during successive pairs. Cardiac imaging method according to claim 1, wherein Black blood signals are acquired by implementing identical black blood acquisition sequences during successive pairs.

3. Cardiac imaging method according to any one of the preceding claims, wherein, processing, by computer, signals acquired during the pairs comprises: • Determining (DET) at least one data relating to a representative area of ​​a cardiac lesion of a black blood reference image from at least one of the black blood images and from at least one of the white blood images.

4. A cardiac imaging method according to claim 3, wherein the black blood reference image is a combination of black blood images.

5. A cardiac imaging method according to any one of claims 3 to 4, wherein at least one data point is determined from white blood signals acquired during only one of the pairs.

6. A cardiac imaging method according to any one of claims 3 to 4, wherein at least one data point is determined from white blood signals acquired during the pairs.

7. A cardiac imaging method according to any one of claims 3 to 6, wherein processing, by computer, signals acquired during the couples comprises: • segmenting, by computer, the reference image in black blood so as to determine the zone (ZI) representative of a cardiac lesion.

8. Cardiac imaging method according to claim 7, wherein determining at least one data point relating to the lesion zone (LZ) comprises: • Generating, by computer, a fused image by replacing pixel or voxel values ​​of a first white blood reference image from white blood signals acquired during at least one of the pairs with new values ​​enabling the representative zone of a cardiac lesion (LZ) to be distinguished from other tissues in the area to be imaged.

9. A cardiac imaging method according to any one of claims 7 to 8, wherein computer processing of signals acquired during the couples comprises: • segment (SEG1), by computer, at least one second white blood reference image generated from white blood signals acquired during at least one of the pairs so as to generate positioning data of a set of at least one wall delimiting the myocardium, • segment (SEG2) the black blood reference image from positioning data of at least one wall (L1, L2) of the set of at least one wall delimiting the myocardium, taken from the positioning data of the set of at least one wall delimiting the myocardium, so as to determine the zone (ZI) representative of a cardiac lesion.

10. A method according to claim 9, wherein the second reference white blood image is segmented using a learning function trained from several sets of training images of the area to be imaged generated from training signals acquired during respective acquisition steps by late gadolinium-enhanced white blood magnetic resonance imaging, each set of training images being obtained from white blood signals acquired by implementing a relaxometry sequence.

11. Imaging system configured to implement the method according to any one of the preceding claims, the system comprising a processing and control unit (TC), a magnetic resonance system (A), the processing and control unit (TC) being configured to control the magnetic resonance system (A) so that the magnetic resonance system (A) implements the acquisition sequence.

12. Imaging system according to the preceding claim, wherein the processing and control unit (TC) is configured to implement the processing step of signals acquired during successive cardiac cycles.

13. Computer program product comprising instructions that lead the system according to any one of claims 11 to 12 to perform the steps of the process according to any one of claims 1 to 10.

14. Computer-readable medium on which the computer program according to claim 13 is recorded.

Images