Method and system for acquiring signals by magnetic resonance for cardiac magnetic resonance imaging

Abstract

The invention relates to a method for acquiring signals by magnetic resonance for cardiac imaging, the method comprising an acquisition sequence (SE), which comprises:  acquiring signals comprising, during each pair consisting of two consecutive cardiac cycles, successive pairs:  acquiring black-blood signals from an area to be imaged of a heart of an individual by black-blood magnetic resonance with late gadolinium enhancement; and  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 the successive pairs being acquired by implementing a relaxometry sequence.

Description

[0001] Description

[0002] METHOD AND SYSTEM FOR ACQUIRING MAGNETIC RESONANCE SIGNALS FOR CARDIAC IMAGING BY MAGNETIC RESONANCE

[0003] Field of invention

[0004] The invention relates to the field of cardiac magnetic resonance imaging (MRI) by late gadolinium enhancement or LGE, with reference to the English expression “Late Gadolinium Enhancement”.

[0005] The reference technique for characterizing regional lesions including myocardial fibrosis is late gadolinium enhancement imaging in white blood (BR-LGE) by inversion recovery such as the PSIR sequence (from the English expression "phase-sensitive inversion-recovery"). In this type of imaging, the signal of the healthy myocardium is canceled using inversion-recovery pulses, which makes it possible to visualize a lesion, particularly of the myocardium, which is an area of ​​gadolinium accumulation, i.e. an area where gadolinium is evacuated more slowly than in the healthy myocardium, with a 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, precise, reliable and robust detection.

[0006] To circumvent this problem, dark blood LGE (BL-LGE) imaging techniques have been proposed. They allow simultaneous cancellation of signals from healthy myocardium, in which gadolinium evacuates rapidly, and from blood, thus providing high contrast between the lesions and healthy myocardium, where gadolinium evacuates rapidly but also with blood. These techniques are particularly useful for identifying focal, i.e., localized, lesions.

[0007] However, dark blood imaging techniques do not allow for the precise localization of myocardial lesions in relation to healthy myocardium, as the contrast between blood and healthy myocardial lesions is not high enough. The inventors of the present application previously proposed in patent application FR2203782 a method for acquiring and merging dark blood and white blood images making it possible to detect and better localize these lesion areas while maintaining a short acquisition time.

[0008] 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 in the acute phase of a cardiac event.

[0009] One aim of the invention is to propose a solution for acquiring signals from which it is possible to detect and locate areas of lesions and to quantify diffuse changes in tissues.

[0010] An aim of an embodiment is to propose a method making it possible to limit at least one of the aforementioned drawbacks.

[0011] To this end, the invention relates to a method for acquiring signals by magnetic resonance for cardiac imaging, the method comprising an acquisition sequence comprising:

[0012] ■ acquire signals including, during each pair, consisting of two consecutive cardiac cycles, successive pairs:

[0013] ■ acquire dark blood signals from an area to be imaged of an individual's heart by dark blood magnetic resonance with late gadolinium enhancement, the dark blood signals making it possible to generate an elementary dark blood image of the area to be imaged,

[0014] ■ acquire white blood signals from the area to be imaged by white blood magnetic resonance by late gadolinium enhancement, the black blood signals making it possible to generate an elementary black blood image of the area to be imaged, the white blood signals acquired during successive pairs being acquired by implementing a relaxometry sequence.

[0015] According to one embodiment, the black-blood signals make it possible to generate an elementary black-blood image of the area to be imaged and the black-blood signals make it possible to generate an elementary black-blood image of the area to be imaged.

[0016] The invention also relates to a method of cardiac imaging by magnetic resonance comprising the acquisition method.

[0017] In other words, the invention relates to a method of cardiac imaging by magnetic resonance, the method comprising an acquisition sequence comprising:

[0018] ■ acquire signals including, during each pair, consisting of two consecutive cardiac cycles, successive pairs:

[0019] ■ acquire dark blood signals from an area to be imaged in an individual's heart by dark blood magnetic resonance with late gadolinium enhancement,

[0020] ■ acquire white blood signals from the area to be imaged by white blood magnetic resonance by late gadolinium enhancement, the white blood signals acquired during successive pairs being acquired by implementing a relaxometry sequence.

[0021] According to one embodiment, the black-blood signals make it possible to generate an elementary black-blood image of the area to be imaged and the black-blood signals make it possible to generate an elementary black-blood image of the area to be imaged.

[0022] According to one embodiment, the black blood signals are acquired by implementing identical black blood acquisition sequences during successive pairs.

[0023] According to one embodiment, the method comprises:

[0024] ■ process, by computer, signals acquired during successive cardiac cycles, including:

[0025] ■ Generate a tissue relaxation time map of the area to be imaged from the white blood signals acquired during successive pairs of cardiac cycles. According to one embodiment, processing, by computer, signals acquired during the pairs comprises:

[0026] ■ generate dark blood images from dark blood signals acquired during successive pairs,

[0027] ■ generate white blood images from the white blood signals acquired during successive pairs.

[0028] According to one embodiment, processing, by computer, signals acquired during the couples comprises:

[0029] ■ Determine at least one data item relating to a representative area of ​​a cardiac lesion from a reference black blood image from at least one of the black blood images and from at least one of the white blood images.

[0030] According to one embodiment, the black blood reference image is a combination of the black blood images.

[0031] According to one embodiment, at least one piece of data is determined from white blood signals acquired during only one of the pairs.

[0032] According to one embodiment, at least one piece of data is determined from white blood signals acquired during a plurality of pairs.

[0033] According to one embodiment, at least one piece of data is determined from the white blood signals acquired during the couples.

[0034] According to one embodiment, processing, by computer, signals acquired during the couples comprises:

[0035] ■ segment, by computer, the reference image in black blood in order to determine the representative area of ​​a cardiac lesion.

[0036] According to one embodiment, determining at least one piece of data relating to the area representative of a cardiac lesion comprises:

[0037] ■ 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 ​​making it possible to distinguish the area representative of a cardiac lesion from other tissues in the area to be imaged.

[0038] According to one embodiment, processing, by computer, signals acquired during the pairs comprising: ■ segmenting, 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,

[0039] ■ possibly segmenting the reference image in 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 representative zone of a cardiac lesion.

[0040] 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 enhancement white blood magnetic resonance, each set of training images being obtained from white blood signals acquired by implementing a relaxometry sequence.

[0041] In one embodiment, the second segmentation comprises selecting pixels of 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.

[0042] In one embodiment, the assembly of at least one wall comprises a second wall delimiting the myocardium and surrounded by the first wall

[0043] In one embodiment, the processing comprises calculating data representative of a percentage of transmurality of the area representative of a cardiac lesion from data derived from the positioning data of the first wall and the second wall.

[0044] In one embodiment, the method comprises displaying, on a screen, a representation of data determined during the processing or an image generated during the processing.

[0045] In one embodiment, the black blood and white blood images are two-dimensional. The invention also relates to an imaging system configured to implement the method according to the invention. 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 such that the magnetic resonance system implements the acquisition sequence.

[0046] The imaging system is for example an acquisition system configured to implement the acquisition step and / or the generation of images and / or mapping of relaxation times and / or the processing of images and / or mapping of relaxation times.

[0047] According to one embodiment, the system comprises a device for measuring at least one signal representative of the cardiac activity of the individual, the processing and control unit being configured to control the magnetic resonance system from measurements of the at least one signal representative of the cardiac activity generated by the measuring device so that the magnetic resonance system implements the acquisition sequence

[0048] According to one embodiment, the system comprises 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.

[0049] According to one embodiment, the processing and control unit is configured to implement the step of processing, by computer, the signals acquired during successive cardiac cycles.

[0050] The invention also relates to a computer program product comprising instructions which cause the system according to the invention to execute the steps of the method according to the invention.

[0051] The invention also relates to a computer-readable medium, on which the computer program, or program product, according to the invention is recorded.

[0052] The invention also relates to a method for processing, by computer, data generated from signals acquired by magnetic resonance for cardiac imaging. According to one embodiment, the method comprises:

[0053] ■ receive images of the black blood images of the white blood images generated from the imaging method according to the invention.

[0054] ■ determine at least one piece of data relating to a representative area of ​​a cardiac lesion of a reference black blood image from at least one of the black blood images and from the white blood signals acquired during the pairs.

[0055] This method may include any step, combination of steps, or feature of the processing step or general processing step of the imaging method described in the patent application.

[0056] According to one embodiment, the method comprises:

[0057] ■ 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,

[0058] ■ and possibly segmenting the reference image in 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 representative zone of a cardiac lesion.

[0059] According to one embodiment, the method comprises:

[0060] ■ Generate a data representation relating to the area and optionally display this visual representation.

[0061] According to one embodiment, the method comprises:

[0062] ■ Receive a tissue relaxation time map of the area to be imaged generated by the imaging method according to the invention

[0063] ■ Generate a visual representation of the mapping and optionally display this visual representation

[0064] According to one embodiment, the visual representation is of the bull's eye type and is generated from positioning data of a set of at least one wall delimiting the myocardium. According to one embodiment, the method comprises the step of propagating the walls detected during the step of generating positioning data of a set of at least one wall delimiting the myocardium on the reference image in black blood ISN on the CA map.

[0065] According to one embodiment, the black blood reference image is a combination of the black blood images.

[0066] According to one embodiment, at least one piece of data is determined from white blood signals acquired during only one of the pairs.

[0067] According to one embodiment, at least one piece of data is determined from white blood signals acquired during a plurality of pairs.

[0068] According to one embodiment, at least one piece of data is determined from the white blood signals acquired during the couples.

[0069] According to one embodiment, the method comprises:

[0070] ■ segment, by computer, the reference image in black blood in order to determine the representative area of ​​a cardiac lesion.

[0071] According to one embodiment, the method comprises the determination of at least one piece of data relating to the area representative of a cardiac lesion comprising:

[0072] ■ 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 ​​making it possible to distinguish the area representative of a cardiac lesion from other tissues in the area to be imaged.

[0073] 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 enhancement white blood magnetic resonance, each set of training images being obtained from white blood signals acquired by implementing a relaxometry sequence.

[0074] In one embodiment, the second segmentation comprises selecting pixels of 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.

[0075] In one embodiment, the assembly of at least one wall comprises a second wall delimiting the myocardium and surrounded by the first wall

[0076] In one embodiment, the processing comprises calculating data representative of a percentage of transmurality of the area representative of a cardiac lesion from data derived from the positioning data of the first wall and the second wall.

[0077] In one embodiment, the method comprises displaying, on a screen, a representation of data determined during the processing or an image generated during the processing.

[0078] In one embodiment, the black blood and white blood images are two-dimensional.

[0079] The invention also relates to a processing system configured to implement the method according to the invention, the system comprising a processing or processing and control unit. This unit implements the steps according to the invention.

[0080] This system is, for example, the processing and control system described in the patent application.

[0081] According to one embodiment, the processing system is a processing device such as a computer in which an image processing computer program is installed. This computer and the computer program are configured to receive images produced by an imaging system, for example that of the invention.

[0082] The imaging system may comprise the processing system. Alternatively, these are two separate systems, for example, two separate devices.

[0083] The invention also relates to a computer program product comprising instructions which cause the processing system to execute the steps of the processing method.

[0084] The invention also relates to a computer-readable medium on which the computer program or computer program product is recorded. According to one embodiment of the invention, the processing method and / or the computer program product form a "plugin", according to English terminology, or an additional component or an extension of existing image processing software.

[0085] In the latter case, the method or computer program product is / are configured to receive the first and second images and configured to produce output data for display within an interface produced by the existing image processing software.

[0086] Brief description of the figures

[0087] Other characteristics and advantages of the invention will emerge on reading the detailed description which follows, with reference to the appended figures, which illustrate:

[0088] Fig. 1: an example of an embodiment of a system according to the invention,

[0089] Fig. 2: a schematic representation of a first elementary signal acquisition sequence for generating black blood images and white blood images used in the method according to the invention,

[0090] Fig. 3: a schematic representation of an example of an MRI acquisition sequence in black blood and white blood carried out over a plurality of cardiac cycles comprising a plurality of elementary acquisition sequences including that of figure 2,

[0091] Fig. 4: a schematic representation of a three-dimensional (3D) heart illustrating different section planes distributed along the major axis of the heart and images generated from signals acquired in one of the section planes, Fig. 5: a flowchart of an example of a method according to the invention,

[0092] Fig. 6: a schematic representation of four images including at the top left a white blood image 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 a black blood image and the representation of the walls transferred to the black blood image,

[0093] Fig. 7: at the top, the bottom left image of figure 6 on which sectors have been represented, at the bottom left a bull's-eye type representation of the size of a representative area of ​​a lesion and at the bottom right a bull's-eye type representation of a transmurality lesion percentage. Description of the invention

[0094] The invention relates to the field of cardiac imaging by magnetic resonance or MRI, late gadolinium enhancement in black blood and white blood.

[0095] The invention relates to a method of cardiac imaging.

[0096] By imaging method is meant a method comprising an acquisition of signals making it possible to generate images and / or a processing of signals to generate images and / or other data, for example a mapping of relaxation times and / or a processing of said images and / or other data.

[0097] A cardiac lesion is defined as an area with a tissue singularity near or within a reference area represented by the myocardium. This area has a modified tissue structure compared to a reference tissue structure, the most representative of the myocardium.

[0098] The modification of the tissue structure results in the contrast medium accumulating for a longer period in the area with the tissue singularity than in the areas with the tissue structure most representative of the myocardium.

[0099] Thus, a representative area of ​​a cardiac lesion in a dark 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.

[0100] For example, this intensity is greater than a predetermined threshold.

[0101] A distinction is made between pathological and benign lesions. For example, some lesions may affect cardiac function. These lesions are called pathological lesions. In another example, the lesions may not affect cardiac function, for example, fat. The location of areas representative of cardiac lesions may therefore be of interest to specialists insofar as they may be an indicator of a cardiac pathology affecting cardiac functionality, for example, a myocardial scar, or fat 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.

[0102] Imaging system Figure 1 schematically represents an exemplary embodiment of an imaging system S according to the invention. The system comprises the hardware and software means for implementing the method according to the invention.

[0103] Advantageously, this system S comprises a set of measuring equipment A including a magnetic resonance (MRI) system B as well as an electrocardiograph referenced ECR in figure 1.

[0104] The system S also includes a processing and control system C comprising a processing and control unit TC and a human-machine interface INT.

[0105] As is known, the MRI B magnetic resonance system comprises a static magnetic field generator GEN_B, a gradient generator GEN_GRAD and a radiofrequency (RF) system D_RF.

[0106] The static magnetic field generator GEN_B comprises a main polarization magnet intended to generate, along a longitudinal axis z, a static magnetic field of substantially uniform polarization in a polarization zone (generally a tunnel) intended to comprise the area of ​​the patient to be imaged, this area to be imaged comprising the heart.

[0107] The patient is a mammal. The mammal is, but is not limited to, a human.

[0108] The gradient generator GEN_GRAD comprises three gradient coils (or solenoids) arranged and configured to vary the intensity of the magnetic field in the polarization zone along the respective orthogonal axes x, y and z fixed with respect to the polarization zone. The choice of the intensities circulating in these coils makes it possible to select, from several possible ones, 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.

[0109] The radiofrequency system D_RF comprises a radiofrequency transmitter comprising coils or solenoids and is capable of generating sequences of radiofrequency (RF) pulses, also called acquisition sequences in the present patent application, comprising preparatory sequences for the magnetization of the area to be imaged and reading sequences during which a radiofrequency receiver RF of the radiofrequency system reads, i.e. receives RF signals from the area to be imaged and generated under the effect of the radiofrequency sequences emitted by the transmitter.

[0110] The radiofrequency receiver may be the radiofrequency transmitter or be different from the radiofrequency receiver. Each of the preparatory and reading sequences comprises at least one radiofrequency pulse of predetermined and adjustable frequency, shape, duration, phase, amplitude.

[0111] The preparatory sequence is configured to excite, i.e. modify the direction of magnetization of the tissues in the area to be imaged.

[0112] The reading sequence is configured to allow the receiver of the radio frequency system to measure the magnetization of the area to be imaged resulting from the preparatory module. The ECR electrocardiograph is intended to acquire an electrocardiogram of the patient.

[0113] The processing and control unit TC is configured to generate commands to the MRI system B, in particular to 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 sections of the area to be imaged. The processing and control unit TC is configured to process RF signals acquired by the radiofrequency system, this processing comprises for example: generating a relaxation time map of the area to be imaged and generating images of the area to be imaged, from reconstruction techniques known to those skilled in the art, and to determine data from these images as we will see in more detail in the remainder of the description.

[0114] Acquisition sequence

[0115] Figure 2 represents an example of a first elementary acquisition sequence Se1 of RF signals. This first elementary acquisition sequence Se1 is one of the elementary sequences of an acquisition sequence SE of RF signals making it possible to generate images of the heart and more precisely of an area to be imaged.

[0116] Figure 2 also shows an electrocardiogram (ECG) E measured by the electrocardiograph ECR during the first elementary acquisition sequence Se1. The area to be imaged includes an area of ​​the patient's heart myocardium and blood.

[0117] Advantageously, the area to be imaged comprises at least one section of the myocardium of the patient's heart taken in one 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.

[0118] The invention relates to a method for acquiring signals by magnetic resonance for cardiac imaging.

[0119] In other words, during this process, signals are acquired that allow images of an individual's heart to be generated.

[0120] 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.

[0121] N is, for example, between 2 and 20 but N can be greater than 20.

[0122] For example, N is equal to 5.

[0123] A first elementary sequence Se1 is represented in figure 2.

[0124] The lower part of Figure 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 Se1.

[0125] The first elementary acquisition sequence Se1 includes an acquisition called black blood ACQ11 followed by an acquisition called white blood ACQ21 which will be described later. The black blood acquisition ACQ11 allows the acquisition of the signals from the area to be imaged to generate an elementary image in black blood IM1, i.e. in black blood contrast, of the area to be imaged.

[0126] White blood acquisition ACQ21 allows the acquisition of signals from the area to be imaged, enabling the generation of an elementary white blood image IM2, i.e. in blood-white contrast of the area to be imaged.

[0127] These steps of acquisition in black blood ACQ11, and in white blood ACQ21 each comprise the emission of sequences of electrical pulses comprising a preparatory module PREP1, PREP21 and a reading module LE1, LE2. In the present patent application, by module is meant a radiofrequency pulse or a sequence of radiofrequency pulses.

[0128] It should be noted that throughout the duration of the first elementary acquisition sequence Se1 and, preferably, throughout the 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 so that it generates a fixed static magnetic field along the z axis.

[0129] The gradient generator GEN_GRAD is advantageously controlled by the processing and control unit TC so that the radiofrequency system D_RF acquires signals coming from a predefined section having a predefined thickness during the first elementary acquisition sequence Se1 and throughout the duration of each of the N elementary sequences Sei with i=1 to N.

[0130] In other words, the signals acquired during the SE acquisition sequence come from the same slice, thus constituting the area to be imaged.

[0131] The first acquisition sequence is a late gadolinium enhancement acquisition sequence implemented following the injection of a Gadolinium-based contrast agent intravenously into the patient, 10 to 15 minutes before the implementation of the acquisition sequences in order to obtain images presenting maximum contrast between lesions and blood but also between lesions and healthy myocardium or areas of tissue structure most representative of the myocardium.

[0132] In the heart, the contrast agent is rapidly eliminated from healthy myocardium, which is poor in extracellular or interstitial tissue, but accumulates for a long time in myocardial lesions. Indeed, gadolinium has an extracellular distribution, that is, it does not cross the membranes of cardiomyocytes.

[0133] Gadolinium has the effect of shortening the T1 relaxation time of the tissues where it accumulates. The relaxation of the magnetization of lesions following a magnetization reversal pulse is thus faster than that of healthy blood and myocardium.

[0134] Black blood acquisition

[0135] First, we try to generate elementary images in black blood IM1. In an image of this type, the intensity of the pixels corresponding to blood and healthy myocardium or reference zone of the myocardium is zero (black pixels), substantially zero or very low.

[0136] In order to generate such an elementary black blood image IM1, the RF system D_RF implements, during the first elementary acquisition sequence Se1, an acquisition step in black blood ACQ11 in inversion-recovery. This black blood acquisition step ACQ11 comprises the emission, by the transmitter of the radiofrequency system, of a longitudinal inversion pulse noted 180° in Figure 2, which switches the longitudinal magnetization of the tissues of the imaged area in the opposite direction, that is to say which reverses the longitudinal magnetization of these tissues. In Figure 2, it can be seen that the magnetization of the area to be imaged changes from Mz to -Mz under the effect of the inversion pulse. Due to the longitudinal relaxation, the longitudinal magnetization of the different tissues present in the area to be imaged increases to return to its initial value, passing through the zero value. Naturally, the relaxation kinetics of different tissues are different.

[0137] As is known per se, the ACQ11 black blood acquisition also includes a PREP1 preparatory module implemented after the 180° longitudinal inversion pulse, for example, an adiabatic module in T1-rho (Tip) of duration noted TSL (also called “Spin Lock Time” in English terminology) “Time of Spin Lock”) or a T2-weighted module, or of the MTC type (acronym for the English expression “Magnetization Transfer Contrast”) or a combination of two of these modules or of these three modules.

[0138] The preparatory module PREP1 is configured so that the longitudinal magnetization of the blood A(B) and that of the healthy myocardium A(M), or more generally of the reference zone of the myocardium, cancel each other out at the same instant te.

[0139] 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 with 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.

[0140] The first acquisition step ACQ11 in inversion-recovery then comprises a reading sequence LE1 comprising a 90° pulse applied at time te and a reading gradient to read the transverse magnetization of the area to be imaged. The inversion time Tl is the duration separating the 180° pulse of the reading sequence LE1 from the acquisition in black blood ACQ1 1 . In order to obtain the best contrast between the myocardial lesions and the blood as well as between the myocardial lesions and the healthy myocardium, the reading sequence LE1 is advantageously started at the time te where the longitudinal magnetizations of the blood and the myocardium cancel each other out in order to generate the image with the best contrast.

[0141] In the example of Figure 2, the LE1 reading module of the black blood acquisition is temporally spaced from the PREP1 preparatory module of the black blood acquisition. Alternatively, the LE1 reading module begins as soon as the PREP1 preparatory module ends. The same applies to the relative temporal positioning between the PREP21 preparatory module of the white blood acquisition and the LE2 reading module of the white blood acquisition.

[0142] In the example of Figure 2, the inversion pulse IMP1 is generated before the preparatory module PREP1. Alternatively, the preparatory module PREP1 is generated before the inversion pulse IMP1.

[0143] White blood acquisition

[0144] The elementary white blood image IM21 of the area to be imaged is generated from signals acquired by implementing, during the first elementary acquisition sequence Se1, the white blood acquisition step ACQ21 comprising a preparatory module PREP21 followed by a reading module LE2.

[0145] The ACQ21 white blood acquisition step then includes a LE2 reading module comprising a reading gradient to read the transverse magnetization of the area to be imaged. This LE2 reading module can be performed in gradient echo or spin echo, just like the LE1 reading module of the LE1 black blood acquisition step. The LE1 and LE2 reading modules can be identical or different.

[0146] The duration D2 separating the reading module LE2 from the preparatory module is defined so that the longitudinal magnetization of the blood A(B) is greater than that of the myocardium, in particular of the healthy myocardium A(M), which leads to the generation of an image in which the pixels or voxels of the blood are white, that is to say with a high luminance or intensity, and in which the pixels of the myocardial tissues are a little less luminous than those of the blood as can be deduced from the curves represented in figure 2. These images allow the cardiac anatomy to be perfectly visualized, making it possible to delineate the myocardium on this image, which is not possible on a black blood image.

[0147] 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.

[0148] Synchronization

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

[0150] For this purpose, the processing and control unit TC advantageously uses the electrocardiogram E to generate trigger commands, i.e. to implement, the acquisition sequences intended for the RF device, the gradient generator and possibly the generator of the main magnetic field.

[0151] The acquisition sequence SE comprises, as visible in figure 3, a plurality of elementary acquisition sequences Sei, with i = 1 to N, where N is greater than 1 where i is the index of the elementary sequence, whose acquisition steps in black blood ACQ1i are identical. i= 1 in figure 2. We will see later that the acquisition steps in white blood ACQ2i of the different elementary acquisition sequences Sei are different.

[0152] Each elementary acquisition sequence Sei is implemented during two consecutive cardiac cycles C1 i, C2i, referenced in figure 2 (where i= 1), constituting a pair CBi of two consecutive cardiac cycles referenced in figure 3. Advantageously, each elementary acquisition sequence Sei is implemented during two consecutive RR intervals.

[0153] Advantageously, these two elementary acquisition sequences Sei are implemented during two consecutive interbeats constituting a pair of consecutive interbeats.

[0154] An interest 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 IM1i and IM2i images generated from the signals acquired during the respective elementary acquisition sequences Sei. In the rest of the text, we call beat, a QRS complex, and an interbeat, a phase of a cardiac cycle located between two consecutive beats.

[0155] Advantageously, the consecutive elementary acquisition sequences Sei are implemented during consecutive CBi interbeat pairs as in the example of figure 3.

[0156] Each Sei elementary acquisition sequence includes:

[0157] - During the first cardiac cycle C1 i of the pair of cardiac cycles CBi, the black blood acquisition step ACQI i;

[0158] - During the second cardiac cycle C2i of the pair of cardiac cycles CBi, the white blood acquisition step ACQ2i.

[0159] 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.

[0160] Advantageously, as shown in Figure 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 C1i, C2i.

[0161] Advantageously, this phase is an interbeat.

[0162] Advantageously, this phase is diastole.

[0163] Advantageously, the acquisition sequence SE and the electrocardiogram E are synchronized so that the reading modules LE1, LE2 of the black blood and white blood acquisition steps ACQ1i, ACQ2i are implemented at the same times of these respective cardiac cycles. C1i, C2i.

[0164] These instants are defined in relation to the same time reference of the cardiac cycles C1i, C2i. The time reference is, for example; the maximum of the QRS complex.

[0165] These instants are separated by the same duration D2' from the maximum of the R wave in the example of figure 2.

[0166] The synchronization of the LE1, LE2 reading modules of the black blood and white blood acquisition sequences ACQ1i, ACQ2i and, as we will see later, of different reading modules of white blood acquisition steps on the one hand and of the different reading modules of black blood acquisition steps on the other hand, makes it possible to generate images of the heart at times when the heart occupies the same position in a fixed reference frame relative to the main magnet, which makes it possible to superimpose the images obtained without any registration being necessary or by carrying out a simple registration.

[0167] As visible in figure 3, the successive elementary acquisition sequences Sei are implemented during successive pairs CBi with i= 1 = N of two consecutive cardiac cycles.

[0168] In the non-limiting example of Figure 3, N=3.

[0169] Advantageously, the acquisition sequence SE comprises a plurality of elementary acquisition sequences Sei implemented during consecutive pairs CBi of two consecutive cardiac cycles.

[0170] In a particular embodiment, the elementary acquisition sequences are implemented during consecutive pairs of two consecutive cardiac cycles.

[0171] 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 duration.

[0172] An 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, i.e. to generate usable results even if the acquisition is interrupted, for example if the apnea stops.

[0173] Relaxometry

[0174] According to the invention, the white blood signals acquired during the different pairs of cardiac cycles are acquired by implementing a relaxometry sequence.

[0175] In other words, the acquisition sequence composed of the N white blood acquisition sequences ACQ2i with i= 1 to N is a relaxometry sequence.

[0176] In this way, it is possible to generate a relaxation time map of the tissues of the area to be imaged from said white blood signals acquired during the N pairs of cardiac cycles. An advantage is to acquire, in a single acquisition sequence and therefore in a limited duration, signals allowing not only to detect and locate an area representative of a cardiac lesion but also to quantify diffuse modifications of the myocardial tissues.

[0177] This helps to limit the workload of 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.

[0178] A relaxometry sequence is understood to mean a sequence composed of successive acquisition sequences differing by predefined characteristic durations in order to enable the acquisition of signals allowing the generation of relaxation time mapping.

[0179] In the example of figures 2 and 3, each PREP2i preparatory module is, for example, an adiabatic sequence in T 1 rho presenting a duration noted TSLi.

[0180] The successive ACQ2i white blood acquisition sequences differ only in the spin-lock durations, also called TSL, an acronym for the Anglo-Saxon expression Time of spin-lock.

[0181] The order i of each acquisition sequence ACQ2i represents the temporal order of implementation of the sequence. Thus, the first acquisition sequence of order 1, Se1 is implemented first and the acquisition sequence of order N, SEN is implemented last.

[0182] For example, the TSLi spin-lock durations of the different elementary acquisition sequences are distributed over the interval from 0 ms to 50 ms.

[0183] Generally speaking, locking times are advantageously between 0 ms and 50 ms or between 0 ms and 100 ms.

[0184] For example, in case N = 5, the TSLi spin-lock durations include 0 ms, 10 ms, 20 ms, 35 ms, 50 ms.

[0185] In the example of Figure 3, the spin-locking duration TSLi of the white blood elementary acquisition sequence ACQ2i increases monotonically with the order i of the acquisition sequence ACQ2i.

[0186] Alternatively, the spin-lock duration varies differently depending on the order i. Thus, the signals acquired by implementing the different elementary white blood acquisition sequences ACQ2i make it possible to generate a map of the T1 p or T1 rho relaxation times, also called spin-lattice relaxation times, of the tissues in the area to be imaged from said white blood signals acquired during the N pairs of cardiac cycles.

[0187] Alternatively, the PREP2i preparatory module of each ACQ2i white blood acquisition sequence is a T2-weighted module.

[0188] 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.

[0189] Thus, the signals acquired by implementing the different sequences of elementary acquisitions in white blood ACQ2i make it possible to generate a mapping 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.

[0190] Advantageously, the elementary acquisition steps in black blood ACQ1 i of the different elementary acquisition sequences Sei for i= 1 to N are identical.

[0191] Advantageously, the durations of the preparatory modules in T2 are between 0 ms and 100 ms.

[0192] Acquisition of cuts

[0193] Advantageously, the processing and control unit TC is configured to generate commands to the MRI system to acquire signals of respective slices distributed along a predefined axis of the heart.

[0194] For this purpose, the acquisition step of the method comprises a plurality of SE acquisition sequences such as that previously described for acquiring signals originating from several sections, knowing that during each SE acquisition sequence signals originating from a single section are acquired.

[0195] The acquisition sequences dedicated to the different sections are advantageously placed successively.

[0196] Alternatively, the acquisition sequences of the sections are interleaved. In other words, the acquisition step comprises an elementary acquisition sequence of an acquisition sequence of a first section followed by an elementary acquisition sequence of an acquisition sequence of a second section followed by another elementary acquisition sequence of the acquisition sequence of the first section.

[0197] The axis is advantageously the major axis of the heart. The sections obtained are then so-called minor axis sections. One advantage is that it allows excellent visualization of the two ventricles. However, the invention also applies to the case where the sections are distributed along another axis of the heart, for example an axis in 2 cavities (long vertical axis) or 4 cavities (long horizontal axis) of the heart. Each section has a thickness defined along the axis and is centered on a predefined cutting plane perpendicular to the axis. By section, is meant in the present application, a slice or layer perpendicular to the axis g and having a predefined thickness along the axis.

[0198] Advantageously, the cuts are contiguous along the axis.

[0199] This is achieved by choosing the commands generated to the gradient generator GEN_GRAD by synchronizing the commands to the gradient generator GEN_GRAD and to the RF system D_RF.

[0200] Advantageously, signals are acquired from adjacent or partially overlapping slices. This allows the heart to be imaged completely.

[0201] 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.

[0202] Figure 4 represents a three-dimensional (3D) view of a heart comprising a plurality of section planes noted PCk distributed along the major axis g, with k = 1 to K, K being an integer greater than 1. The section planes are distributed from the apex AP to the base BA of the heart CO. Consequently, the sections are so-called “minor axis sections”.

[0203] Only the images from the section centered on the cutting plane PCk are shown in Figure 4.

[0204] Preferably, several elementary images in black blood IM1 i and / or several elementary images in white blood IM2i are generated with i= 1 to N, N being an integer greater than or equal to 2 for at least one cutting plane PC, for example for each cutting plane PC. This makes the analysis of the images obtained more robust.

[0205] Alternatively, the system is configured to acquire signals from a volume, for example from the entire heart so as to enable the generation of three-dimensional images.

[0206] Advantageously, the SE acquisition sequence is implemented while the patient is holding his breath. One advantage is that it produces perfectly registered images, which makes it possible to limit, simplify, or eliminate the need for image registration.

[0207] In other words, the patient is in apnea for the entire duration of the acquisition sequence.

[0208] Alternatively, the SE acquisition sequence is implemented in free breathing. Free breathing acquisition has temporal advantages. Indeed, breath-hold acquisition must be rapid, which leads to the need to reduce the acquisition time from a few seconds to a few minutes and often involves having to reduce the area to be imaged, for example by limiting it to a 3D portion. However, since the SE acquisition sequence is synchronized with the ECG so that the reading sequences are implemented at the same time marker of different cardiac cycles, breath-hold acquisition leads to generating a limited number of images.

[0209] Image generation

[0210] The system is configured so that the processing and control unit TC receives the signals acquired during the acquisition step SE.

[0211] More generally, the system is configured so that the processing and control unit TC receives the signals acquired during the acquisition step.

[0212] The processing and control unit TC is configured to implement, by computer, a TRA processing step of the signal(s) acquired during the acquisition sequence SE, as shown in figure 5.

[0213] The processing and control unit TC can be configured to implement a general processing step TRAG which can comprise or processing step TRA several processing steps TRA applied to the signals acquired during different acquisition sequences SE of the acquisition step.

[0214] The elementary processing step TRA comprises the generation GEN, by reconstruction techniques known to those skilled in the art, of images of the area to be imaged.

[0215] For example, we can use the GRAPPA algorithm or the SENSE algorithm (and its iterative version).

[0216] The GEN generation step 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 SE acquisition sequence.

[0217] Advantageously, for each black blood acquisition step ACQ1 i, an elementary black blood image IM1 i is generated from the signals acquired during the black blood acquisition step ACQ1 i and, for each white blood acquisition step ACQ2i, an elementary white blood image IM2i from the signals acquired during the white blood acquisition step ACQ2i.

[0218] The elementary images IM1 i, IM2i are advantageously generated in gray level. Each image comprises a set of unitary elements of pixel or voxel type, each characterized by an intensity I capable of taking a set of N values ​​(N being a finite integer greater than 1) corresponding to N gray levels ranging from 0 to N-1. For example, this value can take 256 values ​​between 0 and 255, but N is not limited to 256. This value can advantageously take 4096 values ​​between 0 and 4095.

[0219] The TC processing and control unit is also advantageously configured to use the elementary images obtained to generate the mapping and / or to determine at least one item of data relating to a zone representative of a cardiac lesion as we will see in more detail in the rest of the text.

[0220] The elementary images generated by the TC processing unit can be displayed on a screen of the human-machine interface INT.

[0221] Recalibration

[0222] Advantageously, the method does not include an image registration step. Alternatively, the image generation step GEN comprises: registering elementary black blood images with each other and / or registering elementary white blood images with each other and / or registering elementary black blood and white blood images with each other. This makes it possible, in particular when the patient is breathing freely during the SE acquisition sequence, to limit the effects of breathing on the position of the heart and therefore to avoid spatial shifts induced by breathing on the images and likely to affect the accuracy and reliability of the analyses of these images or of the combination of these images. Indeed, the rhythm of breathing is a priori different from the heart rate, but even if correlations exist between these two rhythms, it is possible for breathing to accelerate while the heartbeat remains stable or vice versa.

[0223] Advantageously, the method comprises: registering elementary images in black blood from the same section.

[0224] Advantageously, the registration is carried out using a non-rigid image registration algorithm.

[0225] Advantageously, the method comprises the registration of elementary white blood images from the same section.

[0226] Advantageously, the registration is carried out using a non-rigid image registration algorithm.

[0227] Advantageously, the method comprises: registering elementary images in black blood IM1i and in white blood IM2i with each other.

[0228] Advantageously, this registration is carried out using a non-rigid image registration algorithm.

[0229] Advantageously, the method comprises: registering elementary images in white blood and elementary images in black blood of the same section.

[0230] Advantageously, this registration is carried out using a non-rigid image registration algorithm.

[0231] Advantageously, these algorithms are identical. It is possible to choose a different algorithm for processing elementary images in black blood 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 resulting from a plurality of elementary images of the same section. Furthermore, it helps reduce artifacts related to breathing.

[0232] According to a first example, at least one of the non-rigid algorithms is an algorithm based on the method of mutual information between images based on statistical relations. The function to be optimized can be implemented by a statistical similarity criterion. An advantage of this method is that the matching between homologous attributes of images of the same section is independent of their geometric position. Furthermore, this method is particularly effective for registering elementary images presenting different contrasts, such as images of black blood and white blood.

[0233] According to a second example compatible with the first example, at least one of the non-rigid algorithms is based on a transformation model is implemented. The transformation model makes it possible to determine functions making it possible to minimize the difference between two images. The difference can be translated by a geometric error to be minimized. Different approaches can be used such as those based on the extraction from each of the images of geometric primitives or shape descriptors such as salient points, shape singularities or contours. A parametric or non-parametric approach can be used.

[0234] As an example of optimizing a transformation model or a similarity criterion, the least squares method can be used.

[0235] Other optimization methods can be implemented such as gradient descent. However, this latter method applies more particularly to image intensities and is not optimal in the context of the invention since the aim is to optimize the sharpness and contrast of the merged image. Nevertheless, the invention includes this embodiment.

[0236] 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 considered.

[0237] When acquiring three-dimensional images, it is possible to acquire several three-dimensional images of the heart, which can be possibly registered. Mapping of relaxation times

[0238] Advantageously, the TRA processing step comprises a step during which a CTO 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.

[0239] Advantageously, white blood images IM2i with i = 1 to N are used for this purpose.

[0240] More specifically, the CTO generation step of the CA relaxation time mapping includes:

[0241] - determine relaxation times associated with the tissues in the area to be imaged.

[0242] In the case of T1 rho relaxometry, the relaxation time is the TI rho.

[0243] In the case of T2 relaxometry, the relaxation time is T2.

[0244] 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 stages Sei with i= 1 to N of the acquisition sequence SE.

[0245] Advantageously, for this purpose, the intensities of this pixel or voxel are used in the IM2i white blood images with i= 1 to N, whether realigned or not.

[0246] A pixel or voxel occupying the same position in the IM2i white blood images with i = 1 to N is considered to come from the same area (pixel or voxel) of the area to be imaged.

[0247] More precisely, for the pixel or voxel we determine a relaxation curve associated with the signals acquired for this pixel or voxels during the relaxometry sequence.

[0248] The relaxation curve is defined by the following relationship:

[0249] IF(TCARACT = M0e~ TCARACT ' / TRELAX

[0250] Where MO is the equilibrium magnetization (unitless) and TCARACT is the characteristic duration in milliseconds. This duration is the spin-locking duration or TSL for a T1 rho relaxometry sequence and the duration of the T2 preparation module for a T2 relaxometry sequence. TRELAX is the relaxation time associated with the sequence in milliseconds. It is the T1 rho for a T1 rho relaxometry sequence and the T2 for a T2 relaxometry sequence.

[0251] SI(TCARACT) is the intensity of the pixel or voxel measured for the characteristic duration TCARACT.

[0252] 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 this pixel or voxel for the different characteristic durations.

[0253] The relaxation time associated with this decay curve is the relaxation time of the tissue associated with the pixel.

[0254] This step is, for example, implemented by linear regression, for example by a least squares method or by a Levenberg-Marquardt type optimization process.

[0255] Alternatively, the curve is obtained by a dictionary matching method called "dictionary matching" in Anglo-Saxon terminology.

[0256] This step includes, for example, for a pixel or voxel the determination of the relaxation curve which among a set of relaxation curves obtained by simulation is the closest, in the sense of a predetermined criterion, to the intensities (or signals) associated with this pixel or voxel for the different characteristic durations. This set of relaxation curves is obtained by Block simulation or by using an EPG framework (from the English "Extended Phase Graph"). These tools make it possible to simulate the proposed relaxometry sequence for a large set of T 1 rho or T2 values. The simulated signals are compared to the acquired signals. The T 1 rho (or T2) value corresponding to the curve closest to the MRI signals is kept. This process is carried out for all pixels in the image.

[0257] The CTO generation step of the CA mapping makes it possible to obtain a mapping of the relaxation times for all the pixels or voxels of the area to be imaged, i.e. of a white blood image.

[0258] In other words, this step consists of generating, for each pixel or voxel in the area to be imaged, a pixel or voxel / associated relaxation time pair. In other words, a relaxation time is associated with each pixel or voxel in the area to be imaged.

[0259] Alternatively, the mapping includes the relaxation times of all or part of the pixels or voxels of the IM2i white blood image.

[0260] The processing and control unit TC is advantageously configured so as to generate, during a generation step GENR, at least one representation of a data item determined during the general processing step TRAG or during the processing step TRA.

[0261] Advantageously, the processing and control unit TC is configured so as to control the display, during a display step AFFD, of at least one representation or image generated by the processing and control unit TC, either automatically or when a user transmits a command to the processing unit via an input interface INTE of the human-machine interface INT.

[0262] The method advantageously comprises an AFFD display step during which at least one visual representation of the CA mapping is displayed on a screen of an INTS output interface of the system.

[0263] One advantage is that it provides a specialist with an indicator of diffuse tissue changes in the area to be imaged, particularly the myocardium.

[0264] For example, we can display a color image in which the color of each pixel or voxel is defined by its relaxation time. For this purpose, we associate distinct colors for distinct and disjoint relaxation time ranges.

[0265] Alternatively or additionally, a visual representation of the mapping may be displayed in the form of a bull's-eye type representation which will be described later.

[0266] Combination of images

[0267] As a plurality of elementary images of the same section are 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.

[0268] Advantageously, the step of generating the GEN images comprises: combining the white blood images of the section IM2i with i = 1 to N so as to obtain a combined white blood ISB image of the section. Advantageously, the method comprises: combining the black blood images IM1i, for example for i = 1 to N of the section so as to obtain a combined black blood ISN image of the section.

[0269] This helps reduce noise and increase the signal-to-noise ratio or SNR (acronym for the English expression “signal-to-noise ratio”).

[0270] Alternatively, M black blood images IM1i 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 section 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 section so as to obtain the combined white blood image.

[0271] Image combination can be done before, during or after GEN generation of images.

[0272] In one example, the combination is 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.

[0273] Averaging has the advantage of preserving image detail, as it increases the SNR. This technique smooths out noise to reduce residual image artifacts. In addition, averaging improves the bit depth of the digital image beyond what is possible with a single image.

[0274] One advantage of the averaging step of images taken from the same slice is to reduce noise. The noise amplitude decreases as the square root of the number of images used, i.e. with only 4 images, the noise amplitude can be reduced by a factor of two. Using an example of a 2-minute free-breathing acquisition, it is possible to collect 4 to 5 images per slice plane, which provides good noise reduction performance.

[0275] In an exemplary embodiment, the combination of images may alternatively be implemented by motion-compensated iterative reconstruction. In other words, this type of combination is implemented during the reconstruction of the images. Compensated MRI reconstruction techniques are notably described in the following articles: Odille F, et al., “Generalized reconstruction by inversion of coupled systems (GRICS) applied to free-breathing MRI” Magnetic Resonance in Medicine, 2008; and “3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST”, Bustin A, et al, Journal of Cardiovascular Magnetic Resonance, 2020.

[0276] Advantageously, the elementary images of one part black blood and the other part white blood are respectively combined so as to produce a combined black blood image ISN per section and a combined white blood image ISB per section.

[0277] When acquiring three-dimensional images, it is possible to acquire several three-dimensional images of the heart, which can be combined, for example averaged, to obtain a single three-dimensional image of the heart.

[0278] Determination of data relating to a representative area of ​​a cardiac lesion

[0279] Advantageously, the TRA processing step includes:

[0280] - determine DET at least one data item relating to at least one zone ZI representative of a cardiac lesion, or zone of cardiac lesion, from a reference image in black blood from at least one of the elementary images in black blood IM1i with i = 1 to N generated from the signals in black blood acquired during the acquisition sequence SE, from at least one of the images in white blood IM2i with i = 1 to N generated from the signals in black blood acquired during the acquisition sequence SE.

[0281] An advantage is to determine from the same acquisition sequence, not only a map of relaxation times but also data relating to a representative area of ​​a cardiac lesion identified on a dark blood image, enriched using the anatomical characteristics of the white blood image.

[0282] Advantageously, the location data of the ZI zone representative of a cardiac lesion or a location indicator of the ZI zone representative of a cardiac lesion. The ZI zone representative of a cardiac lesion is advantageously obtained by segmentation of the reference image in black blood as we will see later.

[0283] According to one embodiment, the ZI zone representative of a cardiac lesion is an area whose pixels have an intensity greater than a predetermined intensity threshold on the reference image in black blood.

[0284] The threshold can be set prior to the process or can be set based on the intensity range of the black blood reference image.

[0285] The threshold can be global, that is, the same for all pixels or voxels of the black blood reference image.

[0286] Alternatively, the threshold is local. That is, it is defined and may vary depending on the pixels or voxels of the black blood reference image.

[0287] According to one embodiment, the reference black blood image is a combined black blood image ISN obtained by combining the N black blood images IM1 i with i = 1 to N. An advantage is to take advantage of the benefits of combining the black blood images and in particular the limitation of the SNR.

[0288] Advantageously, this combination is an average.

[0289] Alternatively, this combination is of another type described previously.

[0290] Alternatively, the reference black blood image is one of the black blood images IM1 i with i between 1 and N or a combined image obtained by combining M black blood images taken from among the N black blood images IM1 i with i = 1 to N.

[0291] In a particular embodiment, the data is determined from the N white blood images IM2i, for i = 1 to N.

[0292] For example, the data is determined from a reference white blood image from the N white blood images for i = 1 to N.

[0293] The inventors have found that the combined white blood image ISB, in particular the average, makes it possible to obtain 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.

[0294] Alternatively, the data is determined from a single IM2i white blood image, for a given index i. An advantage is to avoid the impact of the variation of the characteristic duration (TSL or echo duration) on the quality of the generated data. Indeed, varying the characteristic duration has the effect of slightly varying the contrast of the white blood images.

[0295] Advantageously, when the relaxometry sequence is a T1 rho relaxometry sequence, a white blood image generated from the white blood signals acquired for a TSL spin-lock duration greater than or equal to 20 ms is used.

[0296] For example, we use the white blood image generated from the signals acquired by implementing the elementary acquisition sequence Sei with the longest spin-locking duration TSL or the preparatory module in T2 with the longest duration among the N elementary acquisition sequences Sei with i = 1 to N. This white blood image is the one with the highest contrast between the blood and the myocardium, which makes it possible to obtain a maximum of anatomical information on the myocardium.

[0297] Advantageously, when the relaxometry sequence is a T1 rho or T1 p relaxometry sequence, a white blood image generated from the white blood signals acquired for a spin-locking duration TSL greater than or equal to 20 ms is used.

[0298] According to another embodiment, the data is determined from G IM2i white blood images taken from among the N white blood images where G is an integer greater than 1 and less than N.

[0299] For example, we use the G IM2i white blood images generated from the white blood signals acquired for the largest TSI spin-locking durations among the N TSI spin-locking durations with i = 1 to N or from the white blood signals acquired for the largest T2 preparation module durations among the N T2 preparation module durations with i = 1 to N.

[0300] Determination of the representative area of ​​a cardiac lesion

[0301] In a particular embodiment of the invention, each ACQ2 white blood acquisition step is distinct from an inversion-recovery sequence. In other words, this sequence is devoid of a pulse of inversion of the longitudinal magnetization of the area to be imaged.

[0302] Therefore, and unlike PSIR imaging, during white blood acquisition, the longitudinal magnetization of the myocardium is not canceled, which makes it possible to obtain images with a stronger contrast between the myocardium and the blood and therefore facilitate image processing.

[0303] Alternatively, each white blood acquisition step is an inversion recovery sequence. This is, for example, a PSIR sequence (from the English expression “phase-sensitive inversion-recovery”).

[0304] Advantageously, the white blood acquisition is configured so that when the LE2 reading module is implemented, the respective longitudinal magnetizations of the healthy myocardium, the blood and the 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 obtained by the configuration of the PREP2i preparatory module and that of the LE2 reading module and by the relative temporal positioning between these two modules.

[0305] As visible in Figure 5, the step of determining DET the data relating to a zone ZI representative of a cardiac lesion advantageously comprises the following steps:

[0306] - a first segmentation SEG1, by computer, of at least one reference image in white blood from at least one of the elementary white blood images IM2i with i = 1 to N so as to generate positioning data of a first wall L1 delimiting the myocardium,

[0307] - 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 segmentation step SEG1, so as to determine the zone ZI representative of a cardiac lesion.

[0308] These steps are implemented by the TC processing and control unit.

[0309] In the rest of the text, we consider, as in the example of figure 5, that the reference image in white blood ISB is a combined image in white blood ISB and that the reference image in black blood ISN is a combined image in black blood ISN.

[0310] One advantage is that it allows for the automatic, reproducible, reliable and precise segmentation of a ZI zone representative of a cardiac lesion.

[0311] Indeed, the segmentation of ISB white blood images generated from signals measured during the ACQ2i white blood acquisition steps, in particular when they are distinct from inversion-recovery sequences, allows the walls delimiting the myocardium to be positioned automatically, robustly, reliably and precisely, because these images present a significant contrast between the myocardium and the blood. Black blood images do not allow obtaining such good results due to the absence of contrast between the healthy myocardium and the blood.

[0312] Segmenting the reference image in black blood to determine, that is to say to identify, extract or delineate, the representative area of ​​a cardiac lesion allows good results to be obtained which would not be possible on its own with the white blood image containing little or no information on the lesions.

[0313] First segmentation

[0314] According to one example, the first segmentation SEG1 is implemented using at least one white blood reference image ISB 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.

[0315] Positioning data relating to a wall corresponds, for example, to the identification of the pixels constituting the wall.

[0316] The result of this segmentation is visible in figure 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.

[0317] The generated images are, for example, in grayscale. The white areas in Figure 6 represent areas that are lighter than the dotted areas, which represent areas that are lighter than the grid areas, which represent areas that are lighter than the bricks. In the example in Figure 6, the heart cavity is the left ventricle, and the first segmentation SEG1 is implemented to delimit the walls L1, L2 of the part of the myocardium surrounding and delimiting the left ventricle.

[0318] It is 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.

[0319] The second wall L2 is the wall delimiting the myocardium and the left ventricle LV. The first wall L1 surrounding the second wall L2 is the external wall, i.e. facing the outside of the left ventricle LV, of the part of the myocardium surrounding the left ventricle LV. This is the repicardium.

[0320] The second wall L2 is the wall of the myocardium delimiting the left ventricle. This is the endocardium.

[0321] In the images in Figure 6, which are sections of the heart, these L1 and L2 walls form closed curves in that they completely surround the left ventricle LV in short-axis sections.

[0322] It is easy to understand that in 3D these walls form surfaces.

[0323] Alternatively, the first segmentation SEG1 is implemented so as to generate positioning data of only one of these two walls, for example the external wall of the myocardium.

[0324] The first segmentation SEG1 is performed by implementing a learning function or algorithm, for example an artificial neural network, to segment a reference white blood image so as to delimit at least one wall of the myocardium surrounding and delimiting a cavity of the heart.

[0325] In a non-limiting example, the learning function is a neural network.

[0326] The artificial neural network used for segmentation is advantageously a convolutional neural network.

[0327] The convolutional neural network is, for example, of the U-Net type. Alternatively, the artificial neural network is of the transformer type, also called a self-attentive model, for example, of the type commonly called "swin transformer" in Anglo-Saxon terminology.

[0328] The neural network, or more generally the learning function, is implemented on two-dimensional (2D) images 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.

[0329] Advantageously, the learning function is a trained learning function.

[0330] Advantageously, the learning function is trained, prior to the implementation of the method according to the invention or 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 comprising a reference white blood image of the heart and more precisely of the area to be imaged, is capable of segmenting it so as to delimit at least one wall of the myocardium surrounding and delimiting a cavity of the heart.

[0331] When the ACQ2i white blood acquisition sequences are distinct from inversion recovery sequences, the learning function is performed from white blood images of the heart generated by implementing acquisition sequences distinct from inversion recovery sequences.

[0332] When the reference white blood image is a combined ISB image, the white blood images used for training are also combined images obtained from the signals acquired during a relaxometry sequence.

[0333] Preferably, this relaxometry sequence is identical to the acquisition sequence consisting of the white blood acquisition sequences ACQ2i with i = 1 to N.

[0334] In a particular embodiment, the learning function is trained, prior to the implementation of the method according to the invention, from several sets of training white blood images of the area to be imaged generated from training signals acquired during respective acquisition steps by white blood magnetic resonance by late gadolinium enhancement.

[0335] The images of the image sets are labeled by specialists, i.e. segmented by specialists, so that the trained learning function receiving input data comprising 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.

[0336] Each training image set is obtained from white blood signals acquired by implementing a relaxometry sequence.

[0337] Preferably, this relaxometry sequence is identical to the acquisition sequence consisting of the white blood acquisition sequences ACQ2i with i = 1 to N.

[0338] In this particular embodiment, the learning function is configured to generate the positioning data from input data comprising the N white blood images IM2i generated from the white blood signals acquired during the acquisition sequence SE.

[0339] The GENR generation step may comprise 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 making it possible to differentiate them from the other tissues of the image.

[0340] The method may comprise a step of displaying this output image.

[0341] Spread

[0342] The method may comprise a step of propagating the walls detected during the first segmentation step SEG1, on the reference image in black blood ISN and / or on the CA mapping.

[0343] In other words, the method may comprise a REP reporting step, i.e. propagation, comprising the identification, on the reference image in black blood ISN or on the mapping, of the pixels or voxels corresponding to the walls L1 and L2 identified during the first segmentation SEG1.

[0344] In Figure 6, a black blood image ISN of the heart section is schematically represented at the bottom left and the black blood image at the bottom right on which the L1 and L2 walls detected during the first segmentation step SEG1 are represented in thick black lines.

[0345] The identification, on the ISN black blood image or on the CA mapping, 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 ISB white blood reference image.

[0346] These pixels or voxels may have the same respective positions on the white blood image and on the black blood image or mapping when we consider that these images are spatially aligned and that these images have the same size and the same resolution.

[0347] A predetermined or calculated spatial shift may alternatively be applied to these pixels or voxels when a spatial shift is estimated to exist between these images.

[0348] The report may include annotation or colorization of pixels or voxels corresponding to the L1 and L2 walls.

[0349] Second segmentation

[0350] The second segmentation is implemented by the CT processing unit.

[0351] The step of determining DET the data relating to the zone ZI representative of a cardiac lesion may comprise a step of detecting DE this zone ZI representative of a cardiac lesion.

[0352] Advantageously, when a zone ZI representative of a cardiac lesion is detected, the second segmentation SEG2 is implemented. In other words, the second segmentation SEG2 can be implemented only on condition that a zone ZI representative of a cardiac lesion is detected during the detection step DE.

[0353] Alternatively, the DE detection step is implemented after the second segmentation step SEG2. Alternatively, the DET determination step is devoid of a DE detection step.

[0354] The detection step will be described later.

[0355] The second segmentation SEG2 uses a reference image in black blood ISN and positioning data of the first wall L1 and possibly those of the second wall L2 from the first segmentation SEG1.

[0356] This positioning data can be positioning data generated during the first segmentation step SEG1 or positioning data from the reporting step REP.

[0357] Alternatively, the second segmentation step SEG2 includes the carryover step.

[0358] The second segmentation SEG2 makes it possible to locate one or more areas representative of cardiac lesions, i.e. to generate location data for these areas.

[0359] These localization data include, for example, the identification or positions of pixels or voxels corresponding to these representative areas of cardiac lesions.

[0360] In other words, the second segmentation SEG2 makes it possible to extract, that is to say to outline, the representative areas of cardiac lesions on the reference image in black blood.

[0361] The second segmentation SEG2 is, for example, implemented by thresholding.

[0362] It advantageously comprises the identification of pixels or voxels having an intensity greater than or equal to the predetermined intensity threshold only in a predetermined search zone Z of the reference image in black blood ISN delimited by the first wall L1 and / or the second wall L2.

[0363] Indeed, as can be deduced from Figure 2, the representative areas of cardiac lesions present, on the dark blood images, a high intensity compared to healthy myocardium and blood.

[0364] This search zone Z is determined from the positioning data of the first wall L1 and possibly those of the second wall L2 from the SEG segmentation. This is for example the zone of the reference image in black blood ISN delimited by the pixels or voxels of the first wall L1 reported on the black blood ISN image and / or the pixels or voxels of the second wall L2 reported on the black blood ISN image.

[0365] Advantageously, the search area of ​​the black blood image is the area surrounded and delimited by the first wall L1.

[0366] In other words, the second segmentation step SEG2 comprises the search for pixels or voxels with an intensity greater than or equal to a predetermined intensity threshold only in the search area delimited and surrounded by the first wall L1 on the reference black blood image ISN. In other words, these pixels or voxels are taken 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 makes it possible to avoid detecting representative areas of lesion beyond the repicardium.

[0367] 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.

[0368] 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 reference image in black blood ISN. This variant has the advantage of identifying pixels or voxels of the myocardium only.

[0369] Alternatively and / or additionally, the second segmentation SEG2 includes searching for pixels with an 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 papillary muscle pixels or voxels.

[0370] Alternatively, the second segmentation is implemented using a neural network, for example, a convolutional neural network trained to segment representative areas of cardiac lesions having predetermined characteristics in an area delimited by the L1 and / or L2 walls when it receives as input the positioning data of the corresponding wall(s) and the reference black blood image, or using at least one active contour segmentation algorithm, i.e. a segmentation algorithm using an active contour model.

[0371] Alternatively, the DET determination step is devoid of the first segmentation step.

[0372] The determination step includes a step of segmenting the reference image in black blood so as to determine the ZI zone representative of a cardiac lesion.

[0373] This step is, for example, carried out by thresholding or using a neural network as described previously.

[0374] The DE detection step comprises detecting the absence or presence of an area representative of a cardiac lesion using an ISN black-blood image and positioning data of at least one wall, for example, of the second wall L2.

[0375] This step generates as output an indication of the presence or absence of an area representative of a cardiac lesion.

[0376] The DE detection step can be performed by thresholding or by using a neural network like the segmentation step. This step can consist of determining whether a number of contiguous pixels or voxels greater than a predetermined threshold have an intensity greater than a predetermined threshold in the area delimited by the L1 wall and / or the L2 wall whose positioning is defined during the first segmentation step SEG1 of the myocardium. An area representative of a cardiac lesion is detected if this condition is verified and the absence of an area representative of cardiac lesion is detected if this condition is not verified.

[0377] The neural network is, for example, a convolutional neural network.

[0378] The neural network is, for example, trained to detect the presence or absence of an area representative of a cardiac lesion in an area delimited by the L1 and / or L2 walls when it receives as input the positioning data of the corresponding wall(s) and the reference image in black blood.

[0379] In Figure 7, the reference image in black blood ISN is represented, on which the limits L1 and L2 identified during the segmentation and reported and delimiting for example the search zone Z, i.e. propagated, on the black blood image ISN are represented in thick lines, as well as the pixels identified as being pixels of the zone ZI representative of a cardiac lesion.

[0380] The DET determination processing step possibly includes at least one of the following steps:

[0381] - CTA calculation of TA size 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,

[0382] - CTT calculation of at least one degree of transmurality TR 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.

[0383] These calculations are performed using a set of at least one black blood image.

[0384] By size of a ZI zone representative of a cardiac lesion, we mean data representative of dimensions of the zone, such as a volume or a surface, for example, or a number of pixels or voxels.

[0385] Size calculation

[0386] The DET determination advantageously includes a CTA calculation step of the size of the ZI zone representative of a cardiac lesion.

[0387] This CTA calculation step comprises the determination of at least one elementary data item representative of the size of at least one zone 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 zone ZI representative of a cardiac lesion obtained during the second segmentation step SEG2.

[0388] An elementary data representative of a size of a ZI zone can be a percentage of a surface of the myocardium occupied by the ZI zone on a SEC sector of the reference image in black blood ISN or a volume or a mass of the ZI zone representative of a cardiac lesion in this SEC sector starting from the axis I parallel to the axis p and passing substantially through the center of the cardiac cavity on the image in black blood ISN and delimited by two rays R starting from the axis I as visible on the image in black blood ISN.

[0389] The percentage of the myocardial surface occupied by the ZI zone representative of a cardiac lesion in the SEC sector can be calculated from the ratio between the number of pixels corresponding to the ZI zone representative of a cardiac lesion in this SEC sector and the number of pixels corresponding to the myocardium in this SEC sector.

[0390] The number of pixels corresponding to the zone ZI representative of a cardiac lesion in this SEC sector can be calculated from the location 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 L1.

[0391] The CTA step may comprise the calculation of data representative of the size of the ZI zone representative of a cardiac lesion in an SEC sector from several elementary data representative of the size of the ZI zone representative of a cardiac lesion calculated, in this SEC sector, for several black blood images ISN distributed along the p axis.

[0392] For example, we calculate a combination or an average of elementary data.

[0393] The volume of the ZI zone representative of a cardiac lesion on a SEC sector 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.

[0394] The size and / or volume are advantageously also calculated from the predetermined resolution of the images.

[0395] 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 may, for example, comprise the division of the reference image in black blood ISN into a first predefined number, equal to 12 in the non-limiting example of FIG. 1, predefined SEC sectors with the same opening angle al pointing towards the I axis and the calculation of the percentage of the surfaces of the myocardium occupied by the ZI zone representative of a cardiac lesion on the different SEC sectors.

[0396] The first number of sectors and the opening angle al 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 more the number of sectors decreases and the opening angle increases.

[0397] The general processing step TRAG advantageously comprises a generation step GENR, by computer, by the processing unit, of a set of at least one representation of data relating to a zone ZI representative of a cardiac lesion and a display step AFFD of at least one representation of the set of at least one representation on a screen of the human-machine interface.

[0398] The generation step GENR comprises, for example, the generation of data representative of the result of the detection step DE, i.e. the absence or presence of zone ZI representative of a cardiac lesion and the display step comprises the display of this data on a screen of the human-machine interface.

[0399] The set of at least one representation advantageously comprises 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.

[0400] For example, as visible in Figure 7, we can generate a bull's eye type representation (also called "Bull's eye" in Anglo-Saxon terminology) of the percentages or data representative of the percentages of the surfaces of the myocardium occupied by the ZI zone representative of a cardiac lesion in different sectors of black blood images ISN taken according to the respective cutting planes PC.

[0401] Bull's eye imaging is defined by the American Heart Association (AHA) and described in the following article: "Standardized Myocardial Segmentation and Nomenclature for Tomographic Imaging of the Heart: A Statement for Healthcare Professionals From the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association.", Manuel D. Cerqueira et al., Circulation, 2002; 105:539-42.

[0402] The bull's-eye representation comprises a plurality of concentric circles CE separated two by two by crowns CO. Each crown CO is assigned to a slice or a set of contiguous slices, knowing that the closer the crown CO corresponds to a slice or a set of slices close to the apex, the closer it is to the center of the circles. Each crown CO is divided into portions of PSE sectors in which are displayed, as in the example of figure 7, the percentages of the surface of the myocardium occupied by a zone ZI representative of a cardiac lesion and calculated for the respective sectors of the black blood image ISN of the corresponding slice or combinations, for example averages, of the percentages calculated from the percentages of the surface of the myocardium occupied by a zone ZI representative of a cardiac lesion calculated for the sectors of the black blood images of the set of corresponding slices.

[0403] Alternatively and / or additionally, the pixel intensity of the different portions of the crowns depends on the calculated percentage. The lower this percentage, the higher the intensity of the corresponding crown.

[0404] For example, in Figure 7, the display is shown in the form of a known bull's-eye type representation of the respective averages of the percentages of the size of the 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), to the mid-ventricle (middle crown) and to the basal zone (outer crowns).

[0405] Portions of sectors associated with a percentage greater than 80% are represented by dotted lines and those associated with a percentage less than or equal to 80% are represented in white.

[0406] When generating a 3D image of the heart, the image is advantageously divided into several layers along the p axis and the same data is calculated as from 2D images from these different layers. According to one embodiment, the generation step comprises a step of generating a bull's-eye type representation from the relaxation time mapping.

[0407] It is advantageous to use maps calculated for different cuts.

[0408] This representation is generated from the CA relaxation time map(s) and L1 and L2 wall positioning data generated during the first SEG1 segmentation.

[0409] The PSE sector portions are then associated with respective relaxation time values ​​or medians or standard deviations of relaxation times.

[0410] Transmurality

[0411] The DET determination step advantageously comprises a step of calculating 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 thickness of the myocardium occupied by a ZI zone representative of a cardiac lesion.

[0412] This CTT calculation step comprises the determination of at least one data representative of the percentage of transmurality of at least one zone ZI representative of a cardiac lesion using data on the location of a lesion and data on the location of the first and / or second walls L1, L2 resulting from the segmentation step.

[0413] This data can be a percentage of the thickness of the myocardium occupied by a ZI zone representative of a cardiac lesion on a sector of the ISN black blood image starting from axis I.

[0414] The percentage of the thickness of the myocardium occupied by the lesion on the sector can be calculated from the ratio between a number of pixels corresponding to the thickness of the zone ZI representative of a cardiac lesion on this sector and the number of pixels corresponding to the thickness of the myocardium on this sector. The number of pixels corresponding to the thickness of the zone ZI representative of a cardiac lesion can be a number obtained from the results of the lesion segmentation step SC or be calculated, for example by thresholding, during the calculation step CTT from the positioning data of L1 and possibly L2 from the first segmentation step SEG1. The number of pixels corresponding to the thickness of the zone ZI representative of a cardiac lesion on a sector can be an average or a maximum of numbers of pixels, corresponding to the thickness of the zone ZI representative of a cardiac lesion, calculated according to different radii of the sector.

[0415] The number of pixels corresponding to the myocardial thickness in a sector can be an average or a maximum of numbers 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.

[0416] The CTT calculation step may, for example, comprise 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 chamber and calculating the percentage of the myocardial surfaces occupied by the lesion on the different sectors.

[0417] The second number of sectors and therefore the opening angle can vary depending on the cutting plane PC. For example, the closer the cutting plane PC is to the apex, the more the number of sectors decreases and the opening angle increases.

[0418] Advantageously the second number is greater than the first number.

[0419] The GEN generation step may comprise the calculation of data representative of transmurality in a sector from several elementary data representative of transmurality calculated in this sector for several ISN black blood images distributed along the p axis.

[0420] For example, we calculate a combination or an average of elementary data.

[0421] The GENR step advantageously comprises the generation of a REPTR representation of data representative of a percentage of transmurality. The AFFD display step advantageously comprises the display of this representation.

[0422] For example, a bull's-eye representation of combinations, e.g., averages, of percentages of lesion transmurality in sectors of contiguous ISN black blood image sets taken along the respective PC planes can be generated. The pixel intensity of this image advantageously, but not necessarily, represents the percentage of transmurality.

[0423] For example, in Figure 7, the display is shown in the form of a bull's-eye REPTR representation of the averages of percentages of 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 cutting planes distributed along the p axis.

[0424] As before, the bull's-eye representation includes a plurality of sector portions whose intensity corresponds to the combination of the percentage of transmurality calculated for this sector portion.

[0425] 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.

[0426] Merged image

[0427] In a particular embodiment, the DET determination step comprises 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 ​​making it possible to distinguish the area representative of a cardiac lesion from the other tissues of the area to be imaged.

[0428] This step can be one of the steps of the generation step.

[0429] This fused image constitutes an indicator of the location of the ZI zone representative of a cardiac lesion.

[0430] The new pixel values ​​are, for example, colors.

[0431] The method advantageously comprises a step of displaying this merged image.

[0432] One advantage is that it allows a specialist to easily locate the representative area of ​​cardiac injury in relation to the anatomy of the myocardium.

[0433] Benefits

[0434] 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.

[0435] Furthermore, by separating two important pieces of information, namely the anatomy of the heart and the representative areas of lesions, on two separate images, namely respectively the white blood images and the black blood images, it allows for the implementation of an automatic process for characterizing the representative areas of lesions. This automation allows for a significant saving in time and reproducibility compared to prior art solutions.

[0436] The black blood and white blood acquisition sequence of the process requires a relatively short acquisition time, especially when acquiring signals to generate 2D images that involve little computation. This advantageously allows the acquisition sequence to be implemented in breath-hold mode and to limit the movements of the heart between images and therefore the corrections to be made, which makes it possible to limit the computing resources and the implementation of the process in real time. This also makes it possible to limit the artifacts altering the readability of the images. These artifacts accentuate the difficulty of reconstructing clear and precise images in order to locate and detect the representative lesion area. Furthermore, long MRI acquisitions are uncomfortable for the patient. A duration of 10 to 20 min is considered a very long duration and it is difficult for the patient to remain within the MRI without moving.

[0437] Furthermore, in the case of 2D image generation, artifacts of an image extracted from a section plane of the 3D image are avoided, which may lead to cases in which it is impossible to discriminate the presence of a representative area of ​​lesion from the presence of blood located near the muscle. Indeed, in some cases, the lesion is so close to the blood, it is called subendocardial, that it is difficult to know, on images presenting artifacts, whether it is a representative area of ​​a lesion, blood or an artifact of the image.

[0438] Furthermore, the proposed solution allows generating a relaxation time map to quantify diffuse tissue changes, which may 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, which avoids many disadvantages such as the prolongation of the examination, additional apneas for the patient, additional effort for the radiographer, and complex analysis because the images of the two sequences are not spatially aligned.

[0439] Material

[0440] From a hardware point of view, the TC or processing and control unit can be seen as a computer interacting with computer programs.

[0441] The TC processing and control unit or processing unit comprises, for example, a computer, comprising a set of at least one processor, and possibly a memory operationally coupled to the computer.

[0442] 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.

[0443] In other words, the computer-readable medium is a tangible medium. In other words, 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.

[0444] For example, the readable medium is an optical disc, a magneto-optical disc, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a random access memory (RAM), a magnetic card or an optical card.

[0445] The readable medium may include an operating system and load the programs according to the invention. It includes registers adapted to record parameter variables created and modified during the execution of the aforementioned programs. A computer program comprising software instructions is then stored on the readable medium.

[0446] Alternatively, program instructions are obtained from an external source and downloaded over a network. This is particularly the case for applications.

[0447] The processing and control unit TC or processing comprises a calculator, i.e. at least one electronic data processing circuit designed to manipulate and / or transform data represented by electronic or physical quantities in registers of the evaluation system and / or memories into other similar data corresponding to physical data in the memories of registers or other types of display devices, transmission devices or storage devices.

[0448] The processing and control unit CT or processing comprises, for example, one or more memories, for storing data, and operatively coupled to the data processing circuit and a reader adapted to read a computer-readable medium.

[0449] The memory(s) are advantageously provided for storing acquired signals and / or data generated during the 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 the processing, for example a fused image.

[0450] The processing and control unit TC or processing unit comprises, for example, at least one computer, for example, a microcomputer, a computer network, an electronic component, a tablet, a smartphone or a personal digital assistant (PDA).

[0451] The steps of the method according to the invention are, for example, performed by causing the processing circuits of the CT processing and control unit to read predetermined programs recorded on hardware 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 to memories. The processing or overall processing step is, for example, performed 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).

[0452] The processing and control unit CT or processing unit comprises at least one computer comprising at least elements listed below: a set of one or more processors (for example at least one central processing unit (CPU) and / or at least one graphics processing unit (GPU) and / or at least one microcontroller and / or at least one digital signal processor (DSP)) AS IC capable of interpreting instructions in the form of a computer program and / or a hardware assembly such as an application-specific integrated circuit (ASIC), an in-field programmable gate array (FPGA), a programmable logic device (PLD) of programmable logic arrays (PLA), a system on chip (SOC), and / or an electronic card in which steps of the method according to the invention are implemented in hardware elements.

[0453] The invention relates to a computer program product comprising the computer-readable medium containing instructions which, when executed by the processing circuit, cause the system S to implement the steps of the method according to the invention.

[0454] The program product may include the computer-readable recording medium.

[0455] The invention also relates to a computer-readable medium, on which the computer program, i.e. the computer program product, is recorded.

[0456] 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 comprises a computer-readable data carrier on which the program instructions are stored or a data carrier signal on which the program instructions are encoded.

[0457] The form of the program instructions is, for example, a source code form, a computer-executable form, or any intermediate form between a source code and a computer-executable form, such as the form resulting from the conversion of the source code via an interpreter, an assembler, a compiler, a linker, or a locator. Alternatively, the program instructions are microcode, firmware instructions, state definition data, integrated circuit configuration data (e.g., VHDL), or object code. The program instructions are written in any combination of one or more programming languages, for example, an object-oriented programming language (C++, JAVA, Python), a procedural programming language (e.g., C language).

[0458] The communication unit comprises at least one communication device enabling communication between the elements of the system and possibly between at least one element of the system and a device external to the system. The communication systems can establish a physical link between elements of the system and / or between an element of the system and a device external to the system and / or a remote communication link (wireless) between elements of the system and / or between an element of the system and a device external to the system.

[0459] The communication device may comprise any hardware, firmware and / or software suitable for communicating information between elements of the device to which the communication device belongs, for example via a data bus, or to an element external to the device. In order to enable data communication between different devices to which, where appropriate, communication devices belong, these devices comprise hardware, firmware and / or software enabling a wired or wireless communication link, for example Wi-Fi, Bluetooth, cellular or Ethernet, to be established between them.

[0460] The INT user interface allows a user to enter data or commands so as to be able to interact with the programs according to the invention.

[0461] The INT user interface includes, for example, an INTS interface and output and an INTE input interface.

[0462] The input interface includes, for example, a keyboard or a pointing interface, such as a mouse, a light pen, a touchpad, a remote control, a voice recognition device, a haptic device.

[0463] The INTS output interface is designed to return information to a user, in a sensory or electrical manner, such as, for example, visually or audibly. The output interface comprises, for example, a display or screen. The AFFD display step may be a step of returning information by a means other than a display or screen.

[0464] The INTS output interface can be the INTE input interface, for example, in the case of a touchscreen tablet.

[0465] Advantageously, the system S or C is configured so that a user can select, by transmitting a command to the processing and control unit TC or processing via the input interface INTS, one or more representations or images generated during the processing step TRAG or TRA so that each selected image or representation is displayed on one or more screens of the output interface INTS during a display step AFFD.

[0466] For example, a user may select, via the input interface, one or more generated representations or images for a predetermined cut.

[0467] Advantageously, a user can select, via the input interface, a predetermined cut taken from a plurality of cuts 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 screens of the output interface or so that the user can select one or more representations and / or one or more images generated for a predetermined cut.

[0468] Alternatively, the processing and control or processing unit is configured to automatically display 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

CLAIMS 1. Method for acquiring magnetic resonance signals for cardiac imaging, the method comprising an acquisition sequence (SE) comprising: ■ acquire signals including, during each pair, consisting of two consecutive cardiac cycles, successive pairs: ■ acquire (ACQ1 i) dark blood signals from an area to be imaged of an individual's heart by dark blood magnetic resonance with late gadolinium enhancement, the dark blood signals making it possible to generate an elementary dark blood image of the area to be imaged, ■ acquire (ACQ2i) white blood signals from the area to be imaged by white blood magnetic resonance by late gadolinium enhancement, the black blood signals making it possible to generate an elementary black blood image of the area to be imaged, the white blood signals acquired during successive pairs being acquired by implementing a relaxometry sequence.

2. Acquisition method according to claim 1, in which the black blood signals are acquired by implementing identical black blood acquisition sequences during successive pairs.

3. Cardiac imaging method comprising an acquisition method according to any one of the preceding claims, comprising: ■ 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 successive pairs of cardiac cycles.

4. Cardiac imaging method according to any one of the preceding claims, in which, processing, by computer, signals acquired during the couples comprises: ■ generate black blood images (IM 1 i) from the black blood signals acquired during successive pairs, ■ generate white blood images (IM2i) from the white blood signals acquired during successive pairs.

5. Cardiac imaging method according to the preceding claim, in which, processing, by computer, signals acquired during the couples comprises: ■ Determine (DET) at least one data item relating to a representative area of a cardiac lesion from a reference black blood image from at least one of the black blood images and from at least one of the white blood images.

6. Cardiac imaging method according to the preceding claim, in which the reference black blood image is a combination of the black blood images.

7. Cardiac imaging method according to any one of claims 5 to 6, in which at least one piece of data is determined from white blood signals acquired during only one of the pairs.

8. Cardiac imaging method according to any one of claims 5 to 6, in which at least one piece of data is determined from the white blood signals acquired during the pairs.

9. Cardiac imaging method according to any one of claims 5 to 9, in which processing, by computer, signals acquired during the couples comprises: ■ segment, by computer, the reference image in black blood in order to determine the zone (ZI) representative of a cardiac lesion.

10. Cardiac imaging method according to claim 9, in which, determining at least one piece of data relating to the lesion zone (ZI) comprises: ■ 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 making it possible to distinguish the zone representing a cardiac lesion (ZI) from other tissues in the area to be imaged.

11. Cardiac imaging method according to any one of claims 9 to 10, in which, processing, by computer, 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 for a set of at least one wall delimiting the myocardium, ■ segmenting (SEG2) the reference image in black blood 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.

12. The method of claim 11, 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 enhancement white blood magnetic resonance, each set of training images being obtained from white blood signals acquired by implementing a relaxometry sequence.

13. Method for processing, by computer, data generated from signals acquired by magnetic resonance for cardiac imaging, the method comprising: ■ receiving images of the black blood images (IM 1 i), white blood images (IM2i) generated from the method according to claim 4, ■ determine (DET) at least one piece of data relating to a representative area of a cardiac lesion from a reference black blood image taken from at least one of the black blood images and from the white blood signals acquired during the pairs.

14. Method according to the preceding claim comprising: ■ 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 for a set of at least one wall delimiting the myocardium, ■ and possibly segmenting (SEG2) the reference image in black blood 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.

15. A method according to any one of the preceding claims, comprising: ■ Generate a data representation relating to the area and optionally display this visual representation.

16. Method according to any one of claims 13 to 15, comprising: ■ Receive (CTO) a tissue relaxation time map (CA) of the area to be imaged generated by the method according to claim 3, ■ Generate a visual representation of the mapping (CA) and optionally display this visual representation 17. Method according to the preceding claim and according to claim 16, in which the visual representation is of the bull's eye type and is generated from positioning data of a set of at least one wall delimiting the myocardium.

18. Method according to any one of claims 16 to 17 and according to claim 14, comprising a step of propagating the walls detected during the step of generating positioning of a set of at least one wall delimiting the myocardium on the reference image in black blood ISN on the CA map.

19. An imaging system configured to implement the method according to any one of claims 1 to 12, 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.

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

21. Computer program product comprising instructions which cause the system according to any one of claims 19 to 20 to execute the steps of the method according to any one of claims 1 to 12.

22. Computer-readable medium, on which the computer program according to claim 21 is recorded.

23. Processing system configured to implement the method according to any one of claims 13 to 18, the system comprising a processing and control unit (TC).

24. Computer program product comprising instructions which cause the system according to any one of claim 23 to execute the steps of the method according to any one of claims 13 to 18.

25. Computer-readable medium, on which the computer program according to claim 24 is recorded.

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