Device for imaging the heart of a patient and associated system

Abstract

The invention relates to a device for imaging the heart of a patient, the heart comprising a myocardium, the device comprising at least one input interface, at least one processing unit and at least one output interface, the at least one processing unit being configured to implement, by means of a computer, a processing step comprising: - generating a first dark-blood image from first dark-blood signals; - generating a first bright-blood image from first bright-blood signals; - generating a second image of either the bright blood or dark blood type from signals of said type; and – based on the first dark-blood image, the first bright-blood image, and the second image of the aforementioned type, determining first data for locating a myocardial lesion and second data for locating adipose tissue.

Description

[0001] DEVICE FOR IMAGING A PATIENT'S HEART AND ASSOCIATED SYSTEM

[0002] Field of invention

[0003] 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”.

[0004] The field of application of the invention relates more particularly to methods and systems for lesion characterization of the heart. This characterization makes it possible, in particular, to guide ablations.

[0005] The gold standard technique for characterizing regional lesions including myocardial fibrosis is bright-blood late gadolinium enhancement (BR-LGE) imaging using inversion recovery such as the PSIR (phase-sensitive inversion-recovery) sequence. In this type of imaging, the viable myocardial signal is canceled using inversion-recovery pulses, allowing lesions to be visualized with high contrast between healthy myocardial tissue and lesions. However, for myocardial lesions adjacent to the blood chambers of the heart (right and left ventricles), the high intensity of the signal from the blood and therefore the low contrast between the lesions and the blood prevents automatic, accurate, reliable, and robust characterization of scars, particularly subendocardial scars.

[0006] To circumvent this problem, dark blood LGE (BL-LGE) imaging techniques have been proposed. They allow simultaneous cancellation of signals from healthy myocardium and blood, thus providing high contrast both between lesions and between blood and between lesions and healthy myocardium. Healthy myocardium is defined as the largest representative area of ​​the myocardium.

[0007] However, dark blood imaging techniques do not allow for correct characterization of lesions, in particular for locating them precisely in relation to the myocardium, since the contrast between the blood and the healthy myocardium 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 better locate scars in the heart while maintaining a short acquisition time.

[0008] In these types of images, however, scars are difficult to distinguish from tissues such as fatty tissue. It is therefore difficult to discriminate scars from such tissues.

[0009] One aim of the invention is to improve the situation.

[0010] To this end, the invention relates to a device for imaging the heart of a patient, the heart comprising a myocardium delimiting a cavity of the heart, comprising: o At least one input interface configured for:

[0011] ■ receive, during a pair of inter-beats comprising two consecutive inter-beats, first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at a first echo duration,

[0012] ■ receive, during the inter-beat pair, the first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration,

[0013] ■ receive, during the inter-beat pair, second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration, o At least one processing unit configured to implement by computer a processing step comprising:

[0014] ■ generation of a first black blood image from the first black blood signals,

[0015] ■ generation of a first white blood image from the first white blood signals,

[0016] ■ generation of a second image of said type from the second signals of said type, ■ determination, from the first black blood image, from the second image of said type and possibly from the first white blood image, of first location data of a myocardial lesion and second location data of adipose tissue, o At least one output interface configured to return the first location data of a myocardial lesion and the second location data of adipose tissue.

[0017] Myocardial lesion and adipose tissue are understood to mean two areas of interest representing tissue singularities near or within a reference area represented by the myocardium. In these areas of interest, gadolinium accumulates preferentially because these areas are made up of interstitial tissue. Thus, these areas of interest are characterized, in the first dark blood image, by a pixel or voxel intensity greater than a predetermined threshold. The use of the second image of said type in combination with the first dark blood image (or alternatively the first white blood image) makes it possible to highlight the adipose tissue and therefore to differentiate the two areas of interest previously mentioned.

[0018] In this application, the term "lesion" of the myocardium is understood to mean a change in local tissue structure with respect to a tissue structure most representative of the myocardium. A distinction is made between pathological and benign lesions. For example, certain lesions may modify the circulation of a nerve impulse in the myocardium. These lesions are referred to here as pathological. In another example, certain lesions may not modify the circulation of a nerve impulse. These lesions are referred to here as benign.

[0019] Thus, the device according to the invention makes it possible to determine and return, from three signals during a pair of consecutive inter-beats, two signals being of the same type among white blood and black blood acquired at two different echo durations, a combination of information relating to the existence and the position of lesions of the myocardium and cardiac adipose tissues. Thus, the device according to the invention makes it possible to implement more precise clinical observations on the basis of a limited quantity of information. Advantageously, the at least one processing unit is configured to, during the processing step: o Generate a first merged image from a first reference white blood image from the first white blood image and the first location data of the myocardial lesion.

[0020] Advantageously: o The at least one input interface is configured to receive, for a number N greater than 1 of inter-beat pairs, a plurality of N first black blood signals by black blood magnetic resonance and a plurality of N first white blood signals by white blood magnetic resonance, o The at least one processing unit is configured to generate N first black blood images and N first white blood images, o the first reference white blood image is a combination of the N first white blood images.

[0021] Thus, the quality of the first reference white blood image and the first reference black blood image is better, with for example a better signal to noise, and the quality of the first merged image is improved.

[0022] Advantageously, the at least one processing unit is configured to: o Generate a first phase image and a first magnitude image from a first reference image originating from the first image of said type, o Generate a second phase image and a second magnitude image from a second reference image originating from the second image of said type, o Generate a fat image from the first and second phase images and the first and second magnitude images, o determine, from the fat image, second adipose tissue location data. A fat image is understood to mean an image comprising only information on adipose tissue. In other words, the high-intensity pixels of the fat image correspond to the location of adipose tissue.Thus, advantageously, by processing the first reference image and the second reference image, corresponding to two different echo durations, it is possible to obtain the second adipose tissue location data.

[0023] Advantageously, the at least one processing unit is configured to: o when determining said first heart lesion location data and said second adipose tissue location data, segment a first reference image from the first white blood image, so as to generate positioning data for a set of at least one wall delimiting the myocardium, o implement a step of geometric lesion and / or fatty characterization of the heart, from the positioning data and the second adipose tissue location data.

[0024] The segmentation of the first reference white blood image allows for automatic, robust, reliable and precise positioning of the walls delimiting the myocardium, because these images present a significant contrast between the myocardium and the blood. Images in black blood do not allow for as good results due to the absence of contrast between the healthy myocardium and the blood.

[0025] The geometric characterization of lesions and / or fat in the heart, which uses, in addition to the positioning data from segmentation, the first reference black blood image, allows good results to be obtained that could not be obtained on its own using the first reference white blood image containing little or no information on cardiac lesions and the adipose tissue of the heart.

[0026] Advantageously, the at least one processing unit is configured to: o Generate a second fused image from the first fused image and the second adipose tissue location data, in which the lesions of the heart are differentiated from the adipose tissue.

[0027] It is thus possible to generate (and therefore subsequently display and visualize) this second fused image where one can differentiate and / or combine information characteristic of the fatty tissues of the heart from other information characteristic of cardiac lesions.

[0028] Advantageously: o the set of at least one wall comprises a first wall delimiting and surrounding the myocardium, o the at least one processing unit is configured to implement, during the geometric lesion and / or fatty characterization, a preliminary segmentation step to locate an area likely to contain lesions of the myocardium and / or adipose tissue on a first reference black blood image from the first black blood image using data from positioning data of the first wall.

[0029] Advantageously, the first myocardial lesion location data are obtained from data obtained during the preliminary segmentation step and the second adipose tissue location data.

[0030] Advantageously, the at least one output interface comprises a display unit configured to display a first representation of the first myocardial lesion location data in a first color and a second representation of the second adipose tissue location data in a second color different from the first color.

[0031] The invention also relates to a magnetic resonance imaging system comprising a magnetic resonance system configured to implement an acquisition step, the acquisition step comprising: o During the pair of inter-beats, acquisition of the first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at said first echo duration, o During the pair of inter-beats, acquisition of the first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration, o During the pair of inter-beats, acquisition of the second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration.

[0032] According to one embodiment: o the first signals make it possible to generate a first black blood image from the first black blood signals, o the second signals make it possible to generate a first white blood image, o the second signals of said type make it possible to generate a second image of said type.

[0033] According to one embodiment, the acquisition step comprises: o receiving, for a number N greater than 1 of interbeat pairs, a plurality of N first black blood signals by black blood magnetic resonance and a plurality of N first white blood signals by white blood magnetic resonance.

[0034] According to one embodiment, the system comprises an imaging device as described previously.

[0035] Advantageously, the magnetic resonance system is configured to implement: a step of acquiring second white blood signals by magnetic resonance by late gadolinium enhancement at the second echo duration, and the at least one processing unit is configured to: o generate a second white blood image from the second white blood signals.

[0036] Advantageously, the magnetic resonance system is configured to implement: o a step of acquiring second black blood signals by magnetic resonance by late gadolinium enhancement at the second echo duration, and in which the at least one processing unit is configured to: o generate a second black blood image from the second black blood signals.

[0037] The invention also relates to a processing device comprising: o at least one input interface configured:

[0038] ■ Receive a first dark blood image generated from first dark blood signals acquired during a pair of interbeats comprising two consecutive interbeats, by dark blood magnetic resonance with late gadolinium enhancement at a first echo duration,

[0039] ■ receive a first white blood image generated from first white blood signals acquired during the inter-beat pair, by white blood magnetic resonance by late gadolinium enhancement at the first echo time,

[0040] ■ receiving a second image of a type among black blood and white blood generated from the second signals of said type acquired by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration. o At least one processing unit configured to implement by computer a processing step comprising:

[0041] ■ determine, from the first black blood image, from the second image of said type and possibly from the first white blood image, first data for locating a myocardial lesion and second data for locating adipose tissue, o At least one output interface configured to return said first data for locating a myocardial lesion and said second data for locating adipose tissue.

[0042] This device comprises a processing unit configured to implement at least one step or combination of processing steps previously described and described in the remainder of the patent application which are carried out from the white blood and / or black blood images or from data generated from these images, in particular the step of determining the first and second location data.

[0043] According to one embodiment of the invention, the processing unit of the processing device is different from the processing unit of the imaging device.

[0044] According to one embodiment, the processing device is a 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 device, for example that of the invention. However, the processing device is compatible with images produced by other imaging devices producing input images of the type the first image and the second image.

[0045] According to one embodiment, the at least one input interface is configured to: o receive a fat image generated from a first and a second phase image and from a first and a second magnitude image, the first phase image and the first magnitude image being generated from a first reference image from the first image of said type, the second phase image and the second magnitude image being generated from a second reference image from the second image of said type, and the processing step comprises: determining, from the fat image, the second adipose tissue location data.According to one embodiment, this device comprises a processing unit configured to implement at least one step or combination of processing steps previously described and described in the remainder of the patent application which are carried out from the fat image or from data generated from the fat image, in particular the determination of the second adipose tissue location data.

[0046] In one embodiment, the at least one processing unit is configured to: o when determining said first heart lesion location data and said second adipose tissue location data, segment a first reference image from the first white blood image, so as to generate positioning data for a set of at least one wall delimiting the myocardium, o implement a step of geometric lesion and / or fatty characterization of the heart, from said positioning data and said second adipose tissue location data.

[0047] In one embodiment, the at least one output interface comprises a display unit configured to display a first representation of the first myocardial lesion location data in a first color and a second representation of the second adipose tissue location data in a second color different from the first color.

[0048] The invention also relates to an imaging method for imaging the heart of a patient, the heart comprising a myocardium delimiting a cavity of the heart, the method comprising an acquisition step comprising for at least one pair of interbeats comprising two consecutive interbeats: o During the pair of interbeats, acquisition of first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at a first echo duration, o During the pair of interbeats, acquisition of first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration, o During the pair of interbeats, acquisition of second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration,the imaging method further comprising a processing step comprising: o generation of a first black blood image from the first black blood signals, o generation of a first white blood image from the first white blood signals, o generation of a second image of said type from the second signals of said type, o determination, from the first black blood image, from the second image of said type and possibly from the first white blood image, of first location data of a myocardial lesion and second location data of adipose tissue.,

[0049] Thus, the method according to the invention makes it possible to generate, from at least an acquisition of three signals during a pair of consecutive inter-beats, two signals being of the same type among white blood and black blood acquired at two different echo durations, a combination of information relating to the existence and position of lesions of the myocardium and cardiac adipose tissue. Thus, the method according to the invention allows more precise clinical observations on the basis of a limited quantity of information.

[0050] Advantageously, the acquisition step comprises: o Acquisition of second white blood signals by magnetic resonance by late gadolinium enhancement at the second echo time, and the processing step comprises: o generation of a second white blood image from the second white blood signals.

[0051] Advantageously, the acquisition step comprises: o Acquisition of second black blood signals by magnetic resonance by late gadolinium enhancement at the second echo time, and the processing step comprises: o generation of a second black blood image from the second black blood signals.

[0052] Advantageously, the processing step comprises: o Generation of a first fused image from a first reference white blood image derived from the first white blood image and the first myocardial lesion location data.

[0053] Advantageously, the acquisition step is implemented for a number N greater than 1 of inter-beat pairs so as to generate N first white blood images and N first black blood images, the first reference white blood image being a combination of the N first white blood images and the first reference black blood image being a combination of the N first black blood images.

[0054] Thus, the quality of the first reference white blood image and the first reference black blood image is better, with for example a better signal to noise, and the quality of the first merged image is improved.

[0055] Advantageously, the processing step comprises: o generation of a first phase image and a first magnitude image from a first reference image originating from the first image of said type, o generation of a second phase image and a second magnitude image from a second reference image originating from the second image of said type, o generation of a fat image from the first and second phase images and the first and second magnitude images, determination, by computer, from the fat image, of said second adipose tissue location data.The invention also relates to a method for acquiring signals for cardiac imaging of a patient's heart, the heart comprising a myocardium delimiting a cavity of the heart, the method comprising an acquisition step comprising for at least one pair of inter-beats comprising two consecutive inter-beats: o During the pair of inter-beats, acquisition of first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at a first echo duration, o During the pair of inter-beats, acquisition of first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration, o During the pair of inter-beats, acquisition of second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration.

[0056] In one embodiment: o the first signals make it possible to generate a first black blood image from the first black blood signals, o the second signals make it possible to generate a first white blood image, o the second signals of said type make it possible to generate a second image of said type

[0057] The invention also relates to a processing method comprising: o Receiving a first black blood image generated from first black blood signals acquired during a pair of interbeats comprising two consecutive interbeats, by black blood magnetic resonance by late gadolinium enhancement at a first echo duration, receiving a first white blood image generated from first white blood signals acquired during the pair of interbeats, by white blood magnetic resonance by late gadolinium enhancement at the first echo duration, receiving a second image of a type among black blood and white blood generated from the second signals of said type acquired by late gadolinium enhancement magnetic resonance at at least one other echo duration different from the first echo duration.determining, from the first black blood image, the second image of said type and possibly the first white blood image, first data for locating a myocardial lesion and second data for locating adipose tissue, possibly returning said first data for locating a myocardial lesion and said second data for locating adipose tissue.

[0058] According to one embodiment, the processing method comprises: receiving a fat image generated from a first and a second phase images and from a first and a second magnitude images, the first phase image and the first magnitude image being generated from a first reference image from the first image of said type, the second phase image and the second magnitude image being generated from a second reference image from the second image of said type, determining, from the fat image, said second adipose tissue location data.

[0059] According to one embodiment: when determining said first heart lesion location data and said second adipose tissue location data, segmenting a first reference image from the first white blood image, so as to generate positioning data for a set of at least one wall delimiting the myocardium, o implementing a step of geometric lesion and / or fatty characterization of the heart, from said positioning data and said second adipose tissue location data.

[0060] According to one embodiment, the processing method comprises displaying a first representation of the first myocardial lesion location data in a first color and a second representation of the second adipose tissue location data in a second color different from the first color.

[0061] According to one aspect, the invention relates to a computer program product comprising the instructions for executing the method of the invention. 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.

[0062] 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.

[0063] A fat image is an image containing only information on adipose tissue. In other words, the high-intensity pixels of the fat image correspond to the location of adipose tissue. Thus, advantageously, by processing the first reference image and the second reference image, corresponding to two different echo durations, it is possible to obtain the second location data of adipose tissue of the heart.

[0064] Advantageously, the determination of the first data for locating the lesion of the heart and the second data for locating the adipose tissues comprises: o segmentation, by computer, of a first reference image from the first white blood image, so as to generate positioning data for a set of at least one wall delimiting the myocardium, the method comprising: o geometric characterization of the lesion and / or fat of the heart, by computer, from the first reference black blood image, the positioning data and the second adipose tissue locating data.

[0065] The segmentation of the first reference white blood image allows for automatic, robust, reliable and precise positioning of the walls delimiting the myocardium, because these images present a significant contrast between the myocardium and the blood. Images in black blood do not allow for as good results due to the absence of contrast between the healthy myocardium and the blood.

[0066] The geometric characterization of lesions and / or fat in the heart, which uses, in addition to the positioning data from segmentation, the first reference black blood image, allows good results to be obtained that could not be obtained on its own using the first reference white blood image containing little or no information on cardiac lesions and the adipose tissue of the heart.

[0067] Advantageously, the processing step further comprises generating a second fused image from the first fused image and the second adipose tissue location data, in which the lesions of the heart are differentiated from the adipose tissue.

[0068] It is thus possible to generate (and therefore display and visualize) this second fused image where one can differentiate and / or combine information characteristic of the adipose tissues of the heart from other information characteristic of cardiac lesions.

[0069] Advantageously: o the set of at least one wall comprises a first wall delimiting and surrounding the myocardium, o the geometric lesion and / or fatty characterization comprises a preliminary segmentation to locate an area likely to contain lesions of the myocardium and / or adipose tissue on a first reference black blood image from the first black blood image using data from positioning data of the first wall.

[0070] Advantageously, the first myocardial lesion location data are obtained from data obtained during the preliminary segmentation step and from said second adipose tissue location data.

[0071] Advantageously, the method comprises displaying, on a screen, a first representation of the first location data of the myocardial lesion and a second representation of the second location data of the adipose tissue, the first and second representations being displayed in different colors.

[0072] Advantageously, the segmentation of the first reference image uses a first learning function to segment the white blood image so as to obtain the positioning data.

[0073] Advantageously, the first learning function is a convolutional neural network.

[0074] Advantageously, the convolutional neural network is of the Transformer type.

[0075] Advantageously, the first learning function is trained from a set of training images of the area to be imaged generated from signals acquired during respective acquisition steps by white blood magnetic resonance by late gadolinium enhancement distinct from inversion recovery sequences.

[0076] Advantageously, the assembly of at least one wall comprises a first wall delimiting and surrounding the myocardium.

[0077] Advantageously, the preliminary segmentation uses a second learning function to segment the first reference dark blood image from the first dark blood image so as to locate an area likely to contain lesions of the myocardium and / or adipose tissue.

[0078] Advantageously, the second learning function is a convolutional neural network.

[0079] Advantageously, the second learning function is trained from a set of training images of the area to be imaged generated from signals acquired during respective acquisition steps by black blood magnetic resonance by late gadolinium enhancement distinct from inversion recovery sequences.

[0080] Advantageously, the second learning function is trained to determine an intensity threshold.

[0081] Advantageously, the preliminary segmentation comprises the selection of the pixels of the first reference black blood image having an intensity greater than a predetermined threshold, the pixels being taken only from among the pixels of the first reference black blood image surrounded by the first wall.

[0082] Advantageously, the assembly of at least one wall comprises a second wall delimiting the myocardium and surrounded by the first wall.

[0083] Advantageously, the geometric lesion and / or fatty characterization of the heart comprises a step of lesion detection and / or adipose tissue detection, and / or a step of geometric lesion characterization and / or a step of adipose tissue characterization.

[0084] Advantageously, the geometric lesion characterization comprises the calculation of data representative of a lesion size from data originating from the positioning data of the first wall and possibly of a second wall surrounded by the first wall, and from the second data of location of the adipose tissues.

[0085] Advantageously, the geometric lesion characterization comprises the calculation of data representative of a percentage of transmurality of the lesion from data originating from the first lesion location data and the positioning data of the first wall and the second wall.

[0086] Advantageously, the first black blood image, the first white blood image, the second black blood image, the second white blood image, the first reference image and the second reference image are two-dimensional.

[0087] The invention also relates to a magnetic resonance imaging system comprising a magnetic resonance system configured to implement the acquisition step and a processing unit configured to implement the processing step of the method described above. Advantageously, the processing unit is configured to implement the step of segmenting the first reference white blood image.

[0088] Advantageously, the processing unit is configured to implement the preliminary segmentation step.

[0089] Advantageously, the processing unit is configured to implement the step of geometric characterization of lesions and / or fat in the heart.

[0090] Advantageously, the system comprises a set of measuring equipment comprising a magnetic resonance imaging device capable of implementing acquisition by magnetic resonance in black blood and acquisition by magnetic resonance in white blood.

[0091] Advantageously, the processing unit is configured to generate commands to the MRI device so that it implements the acquisition by black blood magnetic resonance and the acquisition by white blood magnetic resonance.

[0092] Alternatively and / or additionally, the processing unit is configured to generate the first (respectively second) white blood and black blood images from the signals acquired during the respective acquisitions.

[0093] Advantageously, the system comprises an electrocardiograph configured to acquire an electrocardiogram of the patient during the respective acquisitions.

[0094] The invention also relates to a computer program product comprising instructions which cause the device according to the invention to implement the processing step by computer.

[0095] The invention also relates to a computer-readable medium, on which the computer program or computer program product according to the invention is recorded.

[0096] The invention also relates to an imaging method for imaging the heart of a patient, the heart comprising a myocardium, the method comprising an acquisition step comprising for at least one pair of interbeats comprising two consecutive interbeats: o During the pair of interbeats, acquisition of first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at a first echo duration, o During the pair of interbeats, acquisition of first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration, o During the pair of interbeats, acquisition of second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration,the imaging method further comprising a processing step comprising: o generation of a first black blood image from the first black blood signals, o generation of a first white blood image from the first white blood signals, o generation of a second image of said type from the second signals of said type, o determination, from the first black blood image, from the second image of said type and possibly from the first white blood image, of first location data of a first area of ​​interest and second location data of a second area of ​​interest, said first area of ​​interest and second area of ​​interest having on the first black blood image an intensity greater than a predetermined threshold, and corresponding to tissue structures of different nature.,

[0097] Thus, the method according to the invention makes it possible to obtain location information for tissue structures of a different nature from a reference area such as the heart, on the basis of an acquisition of three signals during a pair of consecutive inter-beats, two signals being of the same type among white blood and black blood acquired at two different echo durations. Thus, the method according to the invention allows more precise clinical observations on the basis of a limited amount of information. Among the tissue structures, scars (or lesions) or even adipose tissue can be listed.

[0098] Advantageously, the tissue structures corresponding to the first and second areas of interest are areas where gadolinium preferentially accumulates. For example, these are tissue structures consisting of interstitial tissues. Thus, the first and second areas of interest are characterized, in the first black blood image, by a pixel or voxel intensity greater than a predetermined threshold.

[0099] Brief description of the figures

[0100] Other characteristics and advantages of the invention will emerge on reading the detailed description which follows, with reference to the appended figures, which illustrate:

[0101] Figure 1 is an exemplary embodiment of a system according to the invention, Figure 2 is a schematic representation of an elementary sequence of signal acquisition for generating first (and / or second and / or other) black blood images and first (and / or second and / or other) white blood images used in the method according to the invention,

[0102] Figure 3 is a schematic representation of an MRI acquisition phase in black blood and white blood carried out over a plurality of heartbeats, Figure 4 is 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,

[0103] Figure 5 is a flowchart of an example of a method according to the invention, Figure 6 is a schematic representation of four images comprising at the top left a white blood image and at the top right a white blood image on which the walls detected during the segmentation step are represented, and at the bottom left a black blood image and the representation of the walls transferred to the black blood image,

[0104] Figure 7 is a schematic representation of three images comprising on the left a black blood image on which the detected lesions and / or fatty tissues of a heart are represented, in the middle a fat image on which the detected fatty tissues are represented, and on the right a second merged image according to the invention on which the detected lesions and the detected fatty tissues are represented in two different representations,

[0105] Figure 8 is, at the top, a black blood image with transferred walls delimiting a lesion, on which sectors have been represented, at the bottom left a bull's-eye type representation of the lesion size and at the bottom right a bull's-eye type representation of a lesion percentage of transmurality.

[0106] Description of invention

[0107] The invention relates to the field of cardiac imaging by magnetic resonance or MRI, late gadolinium enhancement in black blood and white blood.

[0108] The invention relates to a device, an imaging system and an imaging method for characterizing tissue structures of different nature in or near the heart, and more precisely of at least one cardiac muscle, for example the myocardium.

[0109] By imaging method is meant a method comprising an acquisition of signals enabling images to be generated and / or a processing of signals to generate images and / or other data and / or a processing of said images and / or other data.

[0110] In the remainder of this description, and for illustrative purposes, tissue structures of different nature include cardiac lesions and adipose tissue.

[0111] Cardiac injury means damage to a muscle of the myocardium.

[0112] Cardiac injuries can be divided into acute injuries resulting from acute myocardial injury, such as acute myocardial infarction, and chronic injuries characteristic of chronic cardiac pathologies. These injuries are cardiac injuries, for example, of the myocardium or papillary muscles. These injuries include myocardial fibrosis, which frequently develops in the context of hypertrophic or dilated cardiomyopathies, but which also represent a frequent sequelae of inflammatory heart disease or myocardial infarction. The injuries also include myocardial necrosis, i.e., the volumes of myocytes whose cell membrane has been destroyed and the volumes of extracellular and collagen matrices constituting fibrous scars, in the chronic phase of the infarction.

[0113] Imaging system

[0114] Figure 1 schematically represents an exemplary embodiment of a system S according to the invention. The system comprises the hardware and software means for implementing the method according to the invention.

[0115] Advantageously, this system S comprises a set of measuring equipment A comprising a magnetic resonance imaging (MRI) device B as well as an electrocardiograph referenced ECR in figure 1.

[0116] The system S also comprises a processing device C comprising a processing unit TC and a human-machine interface INT. This processing device may be part of the imaging device B or be external to this device, the system is then a device. Alternatively, the system has a distributed architecture.

[0117] In a manner known per se, the MRI imaging device B comprises a static magnetic field generator GEN_B, a gradient generator GEN_GRAD and a radiofrequency (RF) device D_RF.

[0118] 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.

[0119] The patient is a mammal. The mammal is, but is not limited to, a human.

[0120] 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.

[0121] The radiofrequency device D_RF comprises coils or solenoids and is capable of generating MRI acquisition sequences comprising preparatory sequences for the magnetization of the area to be imaged and sequences for reading RF signals from the area to be imaged.

[0122] Each of the preparatory and reading sequences includes at least one radiofrequency pulse of predetermined and adjustable frequency, shape, duration, phase and amplitude.

[0123] The preparatory sequence is configured to excite, that is, to modify the direction of magnetization of the tissues in the area to be imaged.

[0124] The reading sequence is configured to measure the magnetization of the area to be imaged resulting from the preparatory module.

[0125] The ECR electrocardiograph is intended to acquire an electrocardiogram of the patient.

[0126] The processing unit TC is configured to generate commands to the MRI device B, in particular to the RF device D_RF and the gradient generator GEN_GRAD, so that the MRI device generates the predefined acquisition sequences of signals from predefined volumes or sections of the area to be imaged.

[0127] The TC processing unit is also configured to generate images of the area to be imaged from the measured signals, from reconstruction techniques known to those skilled in the art, and to process these images as we will see in more detail in the remainder of the description.

[0128] Acquisition sequence

[0129] Figure 2 represents an example of an elementary acquisition sequence SE1 of an MRI acquisition sequence of RF signals allowing the generation of images of the heart as well as an electrocardiogram (ECG) E measured by the electrocardiograph ECR during the elementary sequence SE1.

[0130] The acquisition sequence comprises a series of elementary acquisition sequences SE1 such as that represented in figure 2. 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 this elementary sequence SE1.

[0131] The elementary acquisition sequence SE1 comprises a so-called black blood acquisition ACQ1 followed by a so-called white blood acquisition ACQ2 which will be described later. The black blood acquisition ACQ1 makes it possible to acquire first black blood signals of the area to be imaged, making it possible to generate a first elementary black blood image IM1 of the area to be imaged and possibly second black blood signals of the area to be imaged, making it possible to generate a second elementary black blood image IMT. The white blood acquisition ACQ2 makes it possible to acquire first white blood signals of the area to be imaged, making it possible to generate a first elementary white blood image IM2 of the area to be imaged and possibly second white blood signals of the area to be imaged, making it possible to generate a second elementary white blood image IM2'.These acquisition stages ACQ1, ACQ2 each include a preparatory sequence also called preparatory module PREP1, PREP2 and a preparatory sequence also called reading module LE1, LE2.

[0132] In the present patent application, by module is meant a step comprising a radiofrequency pulse or a series of radiofrequency pulses.

[0133] It should be noted that throughout the duration of the elementary acquisition sequence SE1 and preferably throughout the duration of the MRI acquisition sequence, the static magnetic field generator GEN_B is controlled by the processing unit TC so that it generates a fixed static magnetic field along the z axis.

[0134] The gradient generator GEN_GRAD is controlled for the processing unit TX so that the radiofrequency device D_RF acquires signals coming from a predefined slice having a predefined thickness during the elementary acquisition sequence SE1.

[0135] The 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 with maximum contrast between the lesions and healthy tissues and blood. In the heart, the contrast is rapidly eliminated from the healthy myocardium, poor in interstitial tissue, but accumulates for a prolonged period in the myocardial lesions. Gadolinium has an extracellular distribution, that is to say it does not cross the membranes of the cardiomyocytes.

[0136] 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.

[0137] Black blood acquisition

[0138] First, we try to generate the first elementary images in black blood IM1. In an image of this type, the intensity of the pixels corresponding to blood and healthy muscle is zero (black pixels) or substantially zero.

[0139] In order to generate such a first elementary image in black blood IM1, the RF device D_RF implements an acquisition step in black blood ACQ1 in inversion-recovery. This black blood acquisition step ACQ1 includes a longitudinal inversion pulse noted 180° in Figure 2, which switches the longitudinal magnetization of the tissues in the imaged area in the opposite direction, i.e. which reverses the longitudinal magnetization of these tissues. In Figure 2, we see that the magnetization of the area to be imaged changes from Mz to -Mz under the effect of the inversion pulse. Due to the longitudinal relaxation, the longitudinal magnetization of the different tissues present in the area to be imaged increases to return to its initial value, passing through the zero value. Naturally, the relaxation kinetics of the different tissues are different.

[0140] In a manner known per se, the ACQ1 black blood acquisition also includes a PREP1 preparatory module implemented after the 180° longitudinal inversion pulse, for example, an adiabatic T1-rho (Tip) module of duration noted TSL (acronym for the English expression "Time of Spin Lock") or a T2-weighted module, or of the MTC type (acronym for the English expression "Magnetization Transfer Contrast") or a combination of two of these modules or of these three modules. The PREP1 preparatory module is configured so that the longitudinal magnetization of the blood A(Blood) and that of the healthy myocardium A(Musk) cancel each other out at the same instant te.

[0141] At this same instant te, the longitudinal magnetization of the A(Cica) lesions is clearly greater than zero. By acquiring the signals from the area to be imaged at this instant te, we obtain an image with a very high contrast between the pixels or voxels corresponding to the blood and healthy myocardium, which are black, and the pixels or voxels corresponding to the lesions, which are generally white.

[0142] The first acquisition step ACQ1 in inversion-recovery then comprises a first reading sequence LE1 comprising a 90° pulse applied at time te and a reading gradient to read the transverse magnetization of the area to be imaged. The inversion time Tl is the duration separating the 180° pulse of the first reading sequence LE1 from the elementary sequence ACQ1. In order to obtain the best contrast between the myocardial lesions and the blood as well as between the myocardial lesions and the healthy myocardium, the reading sequence LE1 is advantageously started at time te where the longitudinal magnetizations of the blood and the myocardium cancel each other out in order to generate the image with the best contrast.

[0143] In the example of Figure 2, the first reading module LE1 of the black blood acquisition is temporally spaced from the preparatory module PREP1 of the black blood acquisition. Alternatively, the first reading module LE1 begins as soon as the preparatory module PREP1 ends. The same applies to the relative temporal positioning between the preparatory module PREP2 of the white blood acquisition and the reading module LE2 of the white blood acquisition.

[0144] 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.

[0145] The first LE1 reading module of the ACQ1 acquisition sequence allows the acquisition of the first black blood signals to generate a first IM1 black blood image.

[0146] In an embodiment called “Black Dixon” which will be detailed later, the second black blood signals are also acquired during the first acquisition step ACQ1, making it possible to generate a second elementary black blood image IMT by a second LET reading sequence of the acquisition sequence ACQ1 starting at an instant having a second echo duration TE2 different from the first echo duration TE1 of the instant at which the first LET reading sequence begins.

[0147] By echo duration of a reading sequence, we mean the duration between the instant of the 90° RF pulse applied at the instant te (denoted TE in the state of the art) by the first reading module LE1 and the echo which will be generated by this pulse and which will be recorded by the reading sequence.

[0148] The second elementary black blood image IMT generated from the second black blood signals is advantageously used with the first elementary black blood image IM1 in an implementation of a Dixon method called “black Dixon” detailed later.

[0149] As will be seen later, the echo times TE1 and TE2 are predetermined so that signals called water signals and signals called fat signals are respectively in phase (e.g., both appearing dark) and out of phase (e.g., one appearing bright and the other appearing dark). The water signals are characteristic of water molecules in the heart and the fat signals are characteristic of adipose tissue. The echo times TE1 and TE2 depend on the characteristics of the system S, in particular the MRI imaging device B, and the characteristics of the 90° pulse applied at time te.

[0150] Advantageously, signals can also be acquired to generate, during the first acquisition step ACQ1, other elementary black blood images at other echo durations different from the first and second echo durations TE1 and TE2. These other echo durations correspond to times when the water signals and the fat signals are either in phase or out of phase. These other elementary black blood images can be used in combination with the first elementary black blood image IM1 and the second elementary black blood image IMT in an implementation of a Dixon method.

[0151] Acquisition in white blood

[0152] The first elementary white blood image IM2 of the area to be imaged is generated from first white blood signals acquired by implementing the white blood acquisition step ACQ2 comprising a preparatory module PREP2 followed by a second reading module LE2 of the white blood acquisition step ACQ2.

[0153] Advantageously, the preparatory module PREP2 is identical to the preparatory module PREP1 of the black blood acquisition step ACQ1, but the invention also applies when these modules are distinct.

[0154] The PREP2 preparatory module is, for example, an adiabatic sequence in T1 rho.

[0155] Alternatively, the PREP2 module includes at least one preparatory sequence taken from a T2-weighted module and an MTC-type preparatory module (acronym for the Anglo-Saxon expression “Magnetization Transfer Contrast”) or a combination of two of these modules or of these three modules.

[0156] The second reading module LE2 comprising a reading gradient for reading the transverse magnetization of the area to be imaged. This second reading module LE2 can be implemented in gradient echo or spin echo, just like the first reading module LE1 of the black blood acquisition step ACQ1. The first and second reading modules LE1 and LE2 can be identical or different.

[0157] The duration D2 separating the second reading module LE2 from the white blood acquisition ACQ2 is defined so that the longitudinal magnetization of the blood A(Blood) is greater than that of the Myocardium A(MUSC) which leads to generating an image in which the pixels or voxels of the blood are white, that is to say with a high luminance, and in which the pixels of the myocardial tissues are a little less luminous than those of the blood as can be deduced from the curves represented in figure 2. These images allow to perfectly visualize the cardiac anatomy allowing to delimit the myocardium on this image, which is not possible on a black blood image.

[0158] The second LE2 reading module of the ACQ2 white blood acquisition allows the acquisition of first white blood signals to generate a first IM2 white blood image.

[0159] In an embodiment called “White Dixon” which will be detailed later, during the white blood acquisition step ACQ2, second white blood signals are acquired by the second reading module LE2, making it possible to generate a second elementary white blood image IM2' at an echo duration TE2 different from the echo duration TE1 of the instant at which the second reading sequence LE2 begins.

[0160] This second elementary white blood image IM2' is advantageously used with the first elementary white blood image IM2 in an application of a Dixon method called "white Dixon" detailed later.

[0161] As with black blood acquisition, the TE1 and TE2 echo times are predetermined so that the water signals and fat signals are, respectively, in phase at the TE1 echo time and out of phase at the TE2 echo time. The water signals are characteristic of the water molecules in the heart and the fat signals are characteristic of the adipose tissues of the heart.

[0162] For example, the second read sequence LE2 begins with the emission of a 90° RF pulse causing the protons to vibrate, generating echoes at different times later than the time of emission of the 90° RF pulse. For example, the echo times TE1 and TE2 correspond to the first and second subsequent times.

[0163] The echo times TE1 and TE2 depend on the characteristics of the system S, in particular the MRI imaging device B, and the characteristics of the 90° pulse applied at time te.

[0164] Advantageously, other elementary white blood images at other echo times different from the echo times TE1 and TE2 can also be generated during the second acquisition step ACQ2. These other echo times correspond to times when the water signals and the fat signals are either in phase or out of phase. These other elementary white blood images can be used in combination with the first elementary white blood image IM2 and the second elementary white blood image IM2' in an implementation of a Dixon method.

[0165] It will be explained subsequently how, by using the images IM1, IM2, and IMT and possibly the other elementary images in black blood in the “Black Dixon” embodiment, or IM2' and possibly the other elementary images in white blood in the “White Dixon” embodiment, it is possible to improve the detection of lesions and their distinction from the adipose tissues of the heart, and therefore their characterization, in particular their sizing.

[0166] Synchronization of acquisition steps with cardiac cycles

[0167] Preferably, the processing unit TC is configured to synchronize the acquisition sequence SE with the electrocardiogram E.

[0168] For this purpose, the TC processing unit uses the electrocardiogram E to generate commands to trigger the acquisition sequences for the RF device, the gradient generator and possibly the main magnetic field generator.

[0169] Advantageously, the acquisition sequence SE comprises, as visible in figure 3, a plurality of elementary acquisition sequences SE i, with i = 1 to N, where N is greater than 1 where i is the index of the elementary sequence, the acquisition steps ACQ1, ACQ2 of which are identical. i = 1 in figure 2.

[0170] Each elementary acquisition sequence SEi is advantageously implemented during two consecutive cardiac cycles, preferably during two consecutive inter-beats C1, C2 referenced in figure 2 constituting a pair of inter-beats CBi referenced in figure 1. An interest is to minimize the acquisition time and therefore to minimize the movements of the heart between the different acquisitions and the spatial shifts between the images IM1 and IM2, and IMT and IM2', as well as possibly the other elementary images in black blood or the other elementary images in white blood where appropriate.

[0171] In the following text, we call a beat a QRS complex, and an interbeat a phase of a cardiac cycle located between two consecutive beats.

[0172] Advantageously, the consecutive elementary acquisition sequences SEi are implemented during consecutive inter-beat pairs CBi.

[0173] Each SEi elementary acquisition sequence includes:

[0174] - During the first inter-beat C1 of the inter-beat pair CBi, the black blood acquisition step ACQ1; - During the second inter-beat C2 of the inter-beat pair CBi, the white blood acquisition step ACQ2.

[0175] 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, and IMT and IM2', as well as possibly the other elementary images in black blood or the other elementary images in white blood if applicable, acquired during the different SEi elementary sequences.

[0176] Advantageously, as shown in Figure 2, the acquisition sequence SE and the electrocardiogram E are synchronized so that the first and second reading modules LE1, LE2 are implemented during the same phase of respective cardiac cycles C1, C2.

[0177] Advantageously, this phase is an inter-beat.

[0178] Advantageously, this phase is diastole.

[0179] Advantageously, the acquisition sequence SE and the electrocardiogram E are synchronized so that the reading modules LE1, LE2 of the black blood and white blood acquisition steps ACQ1, ACQ2 are implemented at the same times of these respective cardiac cycles C1, C2.

[0180] These instants are defined in relation to the same time reference of the cardiac cycles C1, C2. The time reference is, for example; the maximum of the QRS complex.

[0181] These instants are separated by the same duration D2' from the maximum of the R wave in the example of figure 2.

[0182] The synchronization of the first and second reading modules LE1 LE2 of the acquisition sequences in black blood and in white blood ACQ1, ACQ2 and, as we will see later, of different reading modules of acquisition steps in white blood on the one hand and of the different reading modules of acquisition steps in black blood 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.

[0183] The invention also applies when the order of the acquisition steps is different. For example, it is possible to implement several black blood acquisition steps ACQ1 during consecutive inter-beats then several white blood acquisition steps ACQ2 during consecutive inter-beats and vice versa.

[0184] It is also possible to implement at least one ACQ1 black blood acquisition step and at least one ACQ2 white blood acquisition step during the same inter-beat.

[0185] Alternatively, at least one ACQ2 white blood acquisition step is spaced from a temporally closest ACQ1 black blood acquisition step.

[0186] Acquisition of cuts

[0187] Advantageously, the TC processing unit is configured to generate commands to the MRI device to acquire signals from respective slices distributed along a predefined axis of the heart.

[0188] 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.

[0189] Advantageously, the cuts are contiguous along the axis.

[0190] This is achieved by the commands generated by the choice of commands generated to the gradient generator GEN_GRAD by synchronizing the commands to the gradient generator GEN_GRAD and to the RF device D_RF.

[0191] Advantageously, signals are acquired in adjacent or partially overlapping slices. This allows the heart to be imaged completely.

[0192] 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.

[0193] 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”.

[0194] Only the images from the section centered on the cutting plane PCk are shown in Figure 4.

[0195] Preferably, several first (and possibly second and other) elementary images in black blood IM1 k(j) and / or several first (and possibly second and other) elementary images in white blood IM2k(j) are generated with j= 1 to J, J being an integer greater than or equal to 2 for at least one cutting plane PCk, for example for each cutting plane PCk. This makes the analysis of the images obtained more robust.

[0196] 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.

[0197] 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.

[0198] In other words, the patient is in apnea for the entire duration of the acquisition sequence.

[0199] Alternatively, the SE acquisition sequence is implemented in free breathing. Free breathing acquisition has temporal advantages. Indeed, breath-hold acquisition must be rapid, which means that the acquisition time must be reduced from a few seconds to a few minutes and often involves having to reduce the area to be imaged, for example by limiting it to a 3D portion. However, 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 the generation of a limited number of images. Image generation

[0200] The processing unit TC is configured to generate GEN, using reconstruction techniques known to those skilled in the art, images of the area to be imaged.

[0201] For example, we can use the GRAPPA algorithm or the SENSE algorithm (and its iterative version).

[0202] The 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.

[0203] In the 2D case, advantageously, for each black blood acquisition step ACQ1, a first black blood image IM1, and possibly a second black blood image IMT and other black blood images in the “black Dixon” mode, are generated from the signals acquired during the black blood acquisition step ACQ1 and, for each white blood acquisition step ACQ2, a first white blood image IM2, and possibly a second white blood image IM2' and other white blood images in the “white Dixon” mode, from the signals acquired during the white blood acquisition step ACQ2.

[0204] The elementary images IM1, IM2, IMT, IM2' are advantageously generated in gray level. Each image comprises a set of unitary elements of pixel or voxel type, each characterized by an intensity I capable of taking a set of N values ​​(N being a finite integer greater than 1) corresponding to N gray levels ranging from 0 to N-1. For example, this value can take 256 values ​​between 0 and 255, but N is not limited to 256. This value can advantageously take 4096 values ​​between 0 and 4095.

[0205] The TC processing unit is also configured to use the elementary images obtained to characterize lesions of the heart and / or adipose tissue, for example of the myocardium as we will see in more detail later in the text.

[0206] The elementary images generated by the TC processing unit can be intended to be displayed on a screen of the human-machine interface INT. Registration

[0207] Advantageously, the method does not require an image registration step.

[0208] Alternatively, the image generation step GEN comprises the registration of first elementary images in black blood with each other, and / or the registration of second elementary images in black blood with each other, and / or the registration of other elementary images in black blood with each other, and / or the registration of first elementary images in white blood with each other, and / or the registration of second elementary images in white blood, and / or the registration of other elementary images in white blood with each other, and / or the registration of first (and / or second and / or other) elementary images in black blood and in white blood with each other. This makes it possible, in particular when the patient is breathing freely during the acquisition sequence SE, to limit the effects of breathing on the position of the heart and therefore to avoid spatial shifts induced by breathing on the images and likely to affect the precision and reliability of the analyses of these images or of the combination of these images.Indeed, the rhythm of breathing is a priori different from the heart rate, but even if correlations exist between these two rhythms, it is possible that breathing accelerates while the heartbeat remains stable or vice versa.

[0209] Advantageously, the method comprises the registration of first elementary black blood images and / or second elementary black blood images and / or other elementary black blood images of the same section.

[0210] Advantageously, the registration is carried out using a non-rigid image registration algorithm.

[0211] Advantageously, the method comprises the registration of first elementary white blood images and / or second elementary white blood images and / or other elementary white blood images of the same section.

[0212] Advantageously, the registration is carried out using a non-rigid image registration algorithm.

[0213] Advantageously, the method comprises the registration of first elementary images in black blood IM1 and in white blood IM2 with each other. Advantageously, the method comprises the registration of second elementary images in white blood IM2' and second elementary images in black blood IMT of the same section.

[0214] Advantageously, the method comprises the registration of other elementary images in white blood IM2'i and other elementary images in black blood IMTi of the same section.

[0215] Advantageously, this registration is carried out using a non-rigid image registration algorithm.

[0216] Advantageously, these algorithms are identical. It is possible to choose a different algorithm for processing the first, second, and other elementary images in black blood and white blood, but it is preferable to choose the same algorithm for ease of implementation. Registration significantly improves the quality, particularly the contrast, of an image from a plurality of first, second, and other elementary images of the same section. Furthermore, it reduces artifacts related to breathing.

[0217] According to a first example, at least one of the non-rigid algorithms is an algorithm based on the method of mutual information between images based on statistical relationships. The function to be optimized can be implemented by a statistical similarity criterion. An advantage of this method is that the matching between homologous attributes of images of the same section is independent of their geometric position. Furthermore, this method is particularly effective for registering first and / or second and / or other elementary images presenting different contrasts, such as images of black blood and white blood.

[0218] 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. According to an example of optimization of a transformation model or a similarity criterion, the least squares method can be used.

[0219] 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.

[0220] 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.

[0221] When acquiring three-dimensional images, it is possible to acquire several three-dimensional images of the heart, which can be possibly registered.

[0222] Combination of images

[0223] When 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.

[0224] Advantageously, the step of generating the images GEN comprises, for at least one section of index k, for example for each section, the combination of first (or second or other) white blood images of the section IM2k(j), for example for j = 1 to J so as to obtain a first combined white blood image ISBk of the section.

[0225] In the "White Dixon" embodiment, a second combined ISB2k white blood image of the section is also obtained, and possibly other combined white blood images of the section.

[0226] Advantageously, the method comprises, for at least one section, for example for each section, the combination of first (or second or other) black blood images IM1 k(j), for example for j = 1 to J of the section of index k so as to obtain a combined black blood image ISNk of the section. In the “Black Dixon” embodiment, a second combined black blood image ISN2k of the section is also obtained, and possibly other combined black blood images of the section.

[0227] This helps reduce noise and increase the signal-to-noise ratio.

[0228] Image combination can be done before, during or after GEN generation of images.

[0229] 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.

[0230] Averaging has the advantage of preserving image detail, as it increases the signal-to-noise ratio (SNR). This technique smooths out noise to reduce residual image artifacts. In addition, averaging improves the bit depth of the digital image beyond what is possible with a single image.

[0231] One advantage of the averaging step of images taken from the same slice is to reduce the maximum deviation. The noise amplitude decreases as the square root of the number of images used, i.e. with only 4 images, the noise amplitude can be reduced by a factor of two. 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.

[0232] In an exemplary embodiment, the combination of the images can 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. Advantageously, the first (or second or other) elementary images, on the one hand in black blood and on the other hand in white blood, are respectively combined so as to produce a combined black blood image ISNk and a combined white blood image ISBk per slice.

[0233] In the “White Dixon” embodiment, a second combined ISB2k image in white blood of the section is advantageously obtained, and possibly other combined images in white blood of the section.

[0234] In the “Dixon black” embodiment, a second combined ISN2k image in black blood of the section is advantageously obtained, and possibly other combined images in black blood of the section.

[0235] In the following, the term first reference white blood image will be understood to mean an image derived from the first white blood image.

[0236] For example, the first reference white blood image may be a combination of N first white blood images (combined or elementary) generated from ACQ2 white blood acquisitions of respective SEi elementary acquisition sequences, with i = 1 to N, where N is greater than 1 and where i is the index of the elementary sequence.

[0237] Alternatively, the first reference white blood image is a white blood image (combined or elemental).

[0238] Similarly, the first reference black blood image will be understood to mean an image from the first black blood image.

[0239] For example, the first reference black blood image may be a combination of N first black blood images (combined or elementary) generated from black blood acquisitions ACQ1 of respective elementary acquisition sequences SEi, with i = 1 to N, where N is greater than 1 and where i is the index of the elementary sequence.

[0240] Alternatively, the first reference black blood image is a (combined or elementary) black blood image. The terms "second reference white blood image" and "second reference black blood image" are defined in the same way, in relation to the second white blood image and the second black blood image, acquired at the second echo time TE2 of an interbeat pair. 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.

[0241] Characterization of lesions and / or adipose tissue

[0242] As seen previously, during the SE acquisition sequence, we acquire the first black blood signals for the generation of the first black blood elementary images IM1 and the first white blood signals for the generation of the first white blood elementary images IM2.

[0243] Likewise, in the so-called “white Dixon” embodiment, second white blood signals are acquired for the generation of second elementary white blood images IM2' and possibly other elementary white blood images.

[0244] In the so-called "Dixon black" embodiment, second black blood signals are acquired for the generation of second elementary black blood images IMT and possibly other elementary black blood images.

[0245] These first, second and other elementary images IM1, IM2, possibly IMT, and possibly IM2', are generated from the acquired signals.

[0246] Advantageously, but not necessarily, these first, second, and other elementary images IM1, IM2, IMT, IM2', are generated from signals acquired by implementing an acquisition sequence. This acquisition sequence comprises ACQ2 white blood acquisition steps. Each ACQ2 white blood acquisition step is distinct from an inversion-recovery sequence.

[0247] In other words, this sequence is devoid of a pulse of inversion of the longitudinal magnetization of the area to be imaged.

[0248] Therefore, 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 lesions and the blood and therefore to facilitate diagnosis and image processing. Alternatively, at least one white blood acquisition step is an inversion recovery sequence, for example a PSIR sequence.

[0249] 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 achieved by the configuration of the PREP2 preparatory module and that of the LE2 reading module and by the relative temporal positioning between these two modules.

[0250] As seen in Figure 5, the imaging process includes:

[0251] - the determination DET, from a first image in black blood, a first image in white blood, a second image of a type taken from white blood in the “white Dixon” embodiment and black blood in the “black Dixon” embodiment, and possibly another image of a type taken from white blood in the “white Dixon” embodiment and black blood in the “black Dixon” embodiment, first data for locating a myocardial lesion and second data for locating adipose tissue.

[0252] This step is implemented by the processing unit TC which uses for this purpose first or second or other white blood images ISB, ISB2, and black blood images ISN, ISN2 generated by the method previously described.

[0253] In an embodiment called "fusion mode", it is advantageously possible to generate an image where the first data for localizing a myocardial lesion and the second data for positioning adipose tissue are differentiated. In a detailed example later, it will be described that the differentiation can be carried out by using different representative colors or patterns.

[0254] In an embodiment called "segmentation mode", it is advantageously possible to determine quantitative data on the basis of the first myocardial lesion location data and the second adipose tissue positioning data. In this embodiment, an SEG segmentation step and a preliminary SEG segmentation step are advantageously implemented.

[0255] Each first white blood image ISB, or second white blood image ISB2, or other white blood image respectively black blood ISN, ISN2, ISN2, is a first (or second or other) elementary image IM1 k(j), IM2kO) or a first (or second or other) combined image in white blood ISBk, ISB2k, respectively in black blood ISNk, ISN2k.

[0256] In the remainder of the text, we consider, as in the example of figure 5, that each first white blood image ISB, or second white blood image ISB2, or other white blood image is a first (or second or other) combined image in white blood ISBk, ISB2k and that each first black blood image ISN, or second black blood image ISN2, or other black blood image is a first (or second or other) combined image in black blood ISNk, ISN2 k .

[0257] The invention makes it possible to characterize, in an automatic, reproducible, reliable and precise manner, a cardiac lesion, and more precisely cardiac muscles, in particular lesions of the myocardium, as well as adipose tissue.

[0258] Segmentation

[0259] According to one example, a SEG segmentation is implemented using at least one image from the first white blood image ISB, or the second white blood image ISB2 in the case of the “White Dixon” embodiment, so as to generate positioning data of a first wall L1 and a second wall L2 delimiting the myocardium M and surrounding and delimiting a cavity of the heart, the first wall L1 surrounding the second wall L2.

[0260] Positioning data relating to a wall corresponds, for example, to the identification of the pixels constituting the wall.

[0261] The result of this segmentation is visible in Figure 6 schematically representing at the top left a first white blood image of a section of the ISBk heart and at the top right the first white blood image of the section on which the first wall L1 and the second wall L2 obtained during the segmentation step are represented in thick black lines. The generated images are, for example, in grayscale. The dotted, squared and triangular areas in Figure 6 represent areas of greater intensity than the bricks.

[0262] In the example of Figure 6, the heart cavity is the left ventricle and the SEG segmentation is implemented so as to delimit the L1, L2 walls of the part of the myocardium surrounding and delimiting the left ventricle.

[0263] 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.

[0264] 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.

[0265] The second wall L2 is the wall of the myocardium delimiting the left ventricle. This is the endocardium.

[0266] 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.

[0267] It is easy to understand that in 3D these walls form surfaces.

[0268] Alternatively, the segmentation is implemented so as to generate positioning data of only one of these two walls, for example the external wall of the myocardium.

[0269] SEG segmentation is performed by implementing a first learning function or algorithm, for example an artificial neural network, to segment a white blood image so as to delineate at least one wall of the myocardium surrounding and delimiting a cavity of the heart.

[0270] In a non-limiting example, the first learning function is a neural network.

[0271] The artificial neural network used for segmentation is advantageously a convolutional neural network. The convolutional neural network is, for example, of the ll-Net type or of the transformer type also called self-attentive model, for example, of the type commonly called swin transformer.

[0272] The neural network, or more generally the first 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.

[0273] Advantageously, the first learning function is trained, prior to the implementation of the method according to the invention, from white blood images of the heart, generated from signals acquired during respective white blood acquisition steps distinct from inversion recovery sequences, and labeled by specialists, i.e. segmented by specialists, so that the first trained learning function receiving input data comprising a white blood image of the heart, is capable of segmenting so as to delimit at least one wall of the myocardium surrounding and delimiting a cavity of the heart.

[0274] The first learning function is, for example, configured to deliver, from a white blood input image generated from signals acquired during a white blood acquisition step distinct from an inversion recovery sequence, an output image in which the pixels or voxels corresponding to the walls or contours L1 and L2 are colorized in a predetermined intensity or color or in respective predetermined colors.

[0275] Spread

[0276] The method may comprise a step of propagating the walls detected during the SEG segmentation step, on at least a first black blood image (and / or a second or other black blood image in the “Black Dixon” embodiment). In other words, the method may comprise a REP transfer step, i.e. propagation, comprising, the identification, on a first (or second or other) black blood image ISNk, ISN2k, of the pixels or voxels corresponding to the walls L1 and L2 identified during the SEG segmentation. In Figure 6, a first black blood image ISNk of the heart section is schematically represented at the bottom left and the first black blood image at the bottom right on which the walls L1 and L2 detected during the SEG segmentation step are represented in thick black lines.

[0277] The identification, on the first black blood image ISNk, of the pixels or voxels corresponding to the first wall L1 and respectively to the second wall L2 is determined from the positions of the pixels or voxels corresponding to these walls on the first white blood image ISBk.

[0278] These pixels or voxels can have the same respective positions on the first white blood image and on the first black blood image when we consider that these images are spatially aligned and because these images have the same size and the same resolution.

[0279] 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.

[0280] The report may include annotation or colorization of pixels or voxels corresponding to the L1 and L2 walls.

[0281] Advantageously, the characterization comprises segmentation for each first (or second or other in the “White Dixon” embodiment) ISBk white blood image so as to generate respective positioning data obtained from the respective first (or second or other in the “White Dixon” embodiment) ISBk white blood images.

[0282] Advantageously, the characterization comprises the transfer to each first (or second or other in the “Dixon black” embodiment) black blood image ISNk, of the pixels or voxels corresponding to the L1 and L2 walls identified during the SEG segmentation, from one of the first (or second or other in the “Dixon white” embodiment) white blood images ISBk. The pixels or voxels transferred to the different black blood images or combined black blood images are advantageously identified from respective first (or second or other in the “Dixon white” embodiment) white blood images.

[0283] Advantageously, the positioning data used for the transfer to an ISNk black blood image are generated from a first ISBk white blood image (or a second or other ISB2k white blood image) generated from signals measured during the same elementary acquisition sequence.

[0284] Thus, the positioning data generated from a combined white blood image ISBk (or a second or other white blood image ISB2k of order k) are advantageously transferred to a combined black blood image ISNk of order k.

[0285] Geometric characterization step of lesions and / or fat CAR

[0286] The step of geometric characterization of lesions and / or fat CAR consists of characterizing the heart from the point of view of lesions and / or adipose tissues using one or more first (or second or other in the “black Dixon” embodiment) black-blood image(s) ISNk ISN2k, and positioning data of at least one wall, for example of the second wall L2, obtained from one or more first (or second or other in the “white Dixon” embodiment) white-blood images ISBk, ISB2k.

[0287] This step advantageously makes it possible to generate data characterizing the heart from the point of view of lesions and / or adipose tissue.

[0288] This step is implemented by the CT processing unit.

[0289] The CAR characterization step may comprise a step of lesion detection and / or DEG detection of adipose tissues, and / or a step of geometric characterization of CAE lesion and / or a step of CAG characterization of adipose tissues of the heart.

[0290] Advantageously, when a lesion is detected, the CAE lesion characterization step is implemented. In other words, the CAE lesion characterization step can be implemented only on condition that a lesion is detected during the DE detection step.

[0291] Alternatively, the DE detection step is implemented after the CAE lesion characterization step or after one of the steps of this CAE effective lesion characterization step.

[0292] Alternatively, the CAE lesion geometric characterization step is devoid of a DE detection step.

[0293] Advantageously, when adipose tissue is detected, the CAG characterization step of adipose tissues of the heart is implemented. In other words, the CAG characterization step of adipose tissues of the heart can be implemented only on condition that adipose tissue of the heart is detected during the DE detection step.

[0294] Alternatively, the DEG detection step of adipose tissues of the heart is implemented after the CAG characterization step of adipose tissues of the heart or after one of the steps of this CAG characterization step of adipose tissues of the heart.

[0295] Alternatively, the CAG characterization step of heart adipose tissues is devoid of a DEG detection step of heart adipose tissues.

[0296] As mentioned above, in dark blood images, lesions (also called scars) are often difficult to distinguish from tissues such as adipose tissue. Thus, the method according to the invention advantageously makes it possible to initially detect and characterize the adipose tissues of the heart from one or more dark blood images, so as to subsequently distinguish the areas corresponding to one or more lesions.

[0297] Thus, in a preliminary segmentation step SP, an area likely to contain the lesions and / or adipose tissue is detected in at least one black blood image ISNk so as to generate positioning data of at least one cardiac lesion and adipose tissue of the heart possibly present in the figure, the preliminary segmentation SP using positioning data of the first wall L1 and possibly those of the second wall L2, generated during the segmentation step SEG.

[0298] Then, the CAG characterization step of adipose tissues of the heart and / or DEG detection of adipose tissues of the heart are implemented.

[0299] The results of these steps make it possible to implement the CAE lesion characterization and / or DE lesion detection step.

[0300] The CAE lesion characterization step possibly includes the following steps:

[0301] - CTA calculation of CIC lesion size from the positioning data of the first wall L1 and possibly the second wall L2 from the SEG segmentation step, and possibly data from the CAG characterization step of adipose tissue and / or DEG detection of adipose tissue

[0302] - CTT calculation of at least one degree of transmurality TR of the CIC lesion from the first wall L1 and the second wall L2 resulting from the SEG segmentation step.

[0303] These calculations are performed using a set of at least one first (or second and / or other in the “Dixon Black” embodiment) black blood image.

[0304] Lesion size refers to data representative of the dimensions of the lesion, such as a volume or surface area, for example, or a number of pixels or voxels.

[0305] Preliminary SP segmentation

[0306] The preliminary segmentation SP uses one or more first (or second or other in the “Dixon black” embodiment) black blood image(s) ISNk, ISN2k, and positioning data of the first wall L1 and possibly those of the second wall L2 resulting from the SEG segmentation.

[0307] These positioning data may be positioning data generated during the segmentation step SEG or positioning data from the reporting step REP. Alternatively, the preliminary segmentation step SP includes the reporting step.

[0308] This step makes it possible to locate a Zc zone likely to contain both cardiac lesions and adipose tissue, i.e. to generate location data for this Zc zone.

[0309] This location data includes, for example, the identification or positions of the pixels or voxels corresponding to this zone Zc.

[0310] The preliminary segmentation step SP is advantageously implemented by thresholding.

[0311] It advantageously comprises the identification of pixels or voxels having an intensity greater than or equal to a predetermined intensity threshold only in a predetermined area of ​​at least a first (or second and / or other in the “Dixon black” embodiment) black blood image ISNk, ISN2k) delimited by the first wall L1 and / or the second wall L2. Indeed, as can be deduced from figure 2, the lesions and the adipose tissues have, on the black blood images, a high intensity compared to the healthy myocardium and to the blood.

[0312] This area is determined from the positioning data of the first wall L1 and possibly those of the second wall L2 from the SEG segmentation.

[0313] This is for example the area of ​​a first black blood image ISNk delimited by the pixels or voxels of the first wall L1 and / or the pixels or voxels of the second wall L2 reported on this first black blood image ISN k .

[0314] Advantageously, the area of ​​the first black blood image is the area surrounded and delimited by the first wall L1.

[0315] In other words, the preliminary segmentation step SP comprises searching for pixels or voxels of intensity greater than or equal to a predetermined intensity threshold only in the area delimited and surrounded by the first wall L1 on one or more first (or second or other in the "Dixon "black" embodiment) black blood image(s) ISNk, ISN2k or ISNik. In other words, these pixels or voxels are taken only from among the pixels or voxels of an area of ​​the first (or second or other in the "Dixon "black" embodiment) black blood images surrounded and delimited by the first wall L1. This makes it possible to avoid the erroneous detection of lesions beyond the epicardium, by avoiding confusion between lesions and fat surrounding the epicardium and represented, on the first (or second or other in the "Dixon "black" embodiment) black blood images, by high intensity pixels.

[0316] Alternatively, the zone Z is the area of ​​the first (or second or other in the “Dixon “black” embodiment) black blood image(s) ISNk delimited by the first wall L1 and by the second wall L2.

[0317] In other words, the preliminary segmentation SP comprises searching for pixels with an intensity greater than or equal to a predetermined intensity threshold only in the area delimited by the two walls L1 and L2 of one or more first (or second or other in the “black Dixon” embodiment) black blood images ISNk, ISN2k. This variant has the advantage of identifying the pixels or voxels of the lesions and / or the adipose tissues of the myocardium only. Indeed, it happens that patients have necrosis of the papillary muscles (located in the area delimited by the L2 wall). In these patients the muscles are white on the black blood image which can lead to errors in lesion characterization when segmenting the lesions in the entire area delimited by L1.

[0318] Alternatively and / or additionally, the preliminary segmentation SP includes the search for pixels with an intensity greater than or equal to a predetermined intensity threshold only in the area surrounded by the L2 wall. This step makes it possible to identify the pixels or voxels of the papillary muscles only.

[0319] Alternatively, the preliminary segmentation SP is implemented using a second training function, for example, a neural network, for example, a convolutional neural network trained to segment cardiac lesions in an area delimited by the L1 and / or L2 walls when it receives as input the positioning data of the corresponding wall(s) of the first (or second or other in the “Dixon black” embodiment) black blood image, or using at least one active contour segmentation algorithm, i.e., a segmentation algorithm using an active contour model.

[0320] A common detection step DEC is implemented, comprising detecting the absence or presence of the zone Z likely to contain lesions and / or adipose tissue using a first (or second or other in the “Dixon black” embodiment) blood-black image ISNk, ISN2k and positioning data of at least one wall, for example, of the second wall L2. This step generates as output an indication of the presence or absence of this zone Z.

[0321] The common detection step DEC can be carried out by thresholding or by using a neural network such as the SEG segmentation step. It 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 SEG segmentation step of the myocardium. The presence of the Zc zone likely to contain cardiac lesions and / or adipose tissue is detected if this condition is verified and the absence of this Zc zone is detected if this condition is not verified. The neural network is, for example, a convolutional neural network.The neural network is, for example, trained to detect the presence or absence of cardiac lesions in an area delimited by the L1 and / or L2 walls when it receives as input the positioning data of the corresponding wall(s) and the black blood image.

[0322] DEG detection and / or CAG characterization of cardiac adipose tissue

[0323] These steps are implemented from an IG fat image generated from images generated in the method previously described. These steps allow, among other things, the determination of second adipose tissue location data.

[0324] The determination of the second adipose tissue location data is implemented by implementing a Dixon method. The Dixon method is generally used to distinguish in images of biological samples the adipose tissue contained therein from the aqueous areas contained therein. It thus makes it possible to obtain, on the one hand, an image containing only the adipose tissue of the biological sample studied, and on the other hand, an image containing only the aqueous areas of the biological sample studied.

[0325] To do this, the method comprises a step of generating GENG a fat image IG comprising a first step GENG1, a second step GENG2 and a third step COMB which will be detailed below.

[0326] Dixon's method uses as input a first image of the sample under study where the adipose tissues and aqueous areas are in phase, and a second image of the sample under study where the adipose tissues and aqueous areas are out of phase. In a variant, the method also receives at least a third image as input.

[0327] Here we describe the case where only a first image and a second image are received as input.

[0328] In the “White Dixon” embodiment, the first image and the second image consist of the first white blood image ISB, corresponding to an echo time TE1, and the second white blood image ISB2, corresponding to an echo time TE2. In the “Black Dixon” embodiment, the first image and the second image consist of the first black blood image ISN, corresponding to the echo time TE1, and the second black blood image ISN2, corresponding to an echo time TE2.

[0329] Because protons rotate at different speeds in adipose tissue and in water, it is possible to find TE1 and TE2 echo times such that adipose tissue and aqueous areas are either in phase or out of phase. Typically, the time difference between the TE1 echo time and the TE2 echo time is very small, resulting in differences in phase information and very small differences in magnitude information. For example, the TE1 and TE2 echo times are approximately equal to 1.2 ms.

[0330] A first phase image and a first magnitude image are generated in a first step GENG1 from the first image

[0331] 11. A second phase image and a second magnitude image are generated in a second GENG2 step from the second image

[0332] 12.

[0333] The first phase image, the first magnitude image, the second phase image, and the second magnitude image are then combined in a third COMB step to obtain an image called the IG fat image.

[0334] The GI fat image contains information only about the fatty tissues of the sample under study.

[0335] To obtain the IG fat image, information from images where the adipose tissues and aqueous areas are out of phase is typically subtracted from information from images where the adipose tissues and aqueous areas are in phase.

[0336] For a detailed description of the combination of the first phase image, the first magnitude image, the second phase image, and the second magnitude image to obtain the IG fat image, refer to the article “Multiecho Dixon fat and water separation method for detecting fibrofatty infiltration in the myocardium,” Kellman P, Hernando D, Shah S, et al., Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2009, vol. 61, no. 1, pp. 215-221. In the variant where the method also receives a third image as input, the first, second, and third images are combined to obtain the IG fat image. An example of the combination is described in the article “Three-point Dixon technique for true water / fat decomposition with b inhomogeneity correction,” GH Glover and E. Schneider, Magnetic Resonance in Medicine, 18, 1991.

[0337] Once the GI fat image is generated, it can be processed to detect and characterize the fatty tissue visible there.

[0338] The DEG detection step comprises detecting the absence or presence of adipose tissue using the fat image IG and positioning data of at least one wall, for example, the second wall L2. It outputs an indication of the presence or absence of adipose tissue.

[0339] The DEG detection step can be performed by thresholding or using a neural network as for the preliminary SP 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.

[0340] Advantageously, a step of propagation of the walls detected during the SEG segmentation step on the IG fat image can be implemented, so as to differentiate the epicardial adipose tissues from the adipose tissues which have infiltrated inside the myocardium and to focus on the latter during a clinical observation. In this case, a second REP reporting step, i.e. propagation, comprising the identification, on an IG fat image, of the pixels or voxels corresponding to the L1 and L2 walls identified during the SEG segmentation is implemented.

[0341] The DEG detection step advantageously comprises the identification of pixels or voxels having an intensity greater than or equal to a predetermined intensity threshold only in a predetermined area of ​​the fat image delimited by the first wall L1 and / or the second wall L2. In other words, it is determined whether a number of contiguous pixels or voxels greater than a predetermined threshold have an intensity greater than a predetermined threshold in this area. The presence of myocardial-specific adipose tissue is detected if this condition is verified and the absence of myocardial-specific adipose tissue is detected if this condition is not verified.

[0342] The neural network is, for example, a convolutional neural network. The neural network is, for example, trained to detect the presence or absence of fatty tissue in the heart 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 IG fat image.

[0343] In Figure 7, in the middle, an IG fat image is shown, where the pixels where a presence of adipose tissue specific to the G myocardium are represented by diagonal hatching. These pixels come from the DEG detection step implemented on the IG fat image.

[0344] The pixels in the GI fat image where the presence of adipose tissue is detected can be identified. For example, the coordinates of these pixels can constitute the second adipose tissue location data.

[0345] DE lesion detection and / or CAE lesion geometric characterization

[0346] These steps use the first myocardial lesion location data and the second adipose tissue location data as will be described below.

[0347] In the “fusion mode” embodiment, a first fused image is generated by combining a first reference white blood image as defined above with the first myocardial lesion location data. For example, the first myocardial lesion location data may correspond to the pixel coordinates of a first black blood image whose intensity is greater than a certain value. As mentioned above, in fact, on the black blood images, the contrast is very high between the pixels or voxels corresponding to the blood and healthy myocardium which are black and the pixels or voxels corresponding to the lesions and adipose tissues, which are generally white. Then, a second fused image is generated by combining the first fused image with the second adipose tissue positioning data.

[0348] For example, as seen previously, the pixels of the IG fat image where the presence of adipose tissue is detected can be identified by their coordinates which can advantageously constitute the second adipose tissue location data. These pixels can be colored in a first color.

[0349] Also, the pixels corresponding to the first myocardial lesion location data can be colored in a second color different from the first color.

[0350] Since the white blood images allow the cardiac anatomy to be perfectly visualized, the second merged image advantageously includes the cardiac anatomy, pixels or voxels colored in the first color corresponding to adipose tissue, and pixels or voxels colored in the second color corresponding to lesions and / or adipose tissue.

[0351] By playing in particular on the contrast of the first reference black blood image and the fat image, for example by adjusting the detection threshold, it is possible to adjust the distribution of colors in the second merged image in order to accentuate the visualization of a particular structure.

[0352] Thus, in the event of overlapping certain high intensity pixels of the first reference black blood image with other colored pixels of the fat image, it is possible to increase the intensity of one color among the first color and the second color in order to preferentially distinguish cardiac lesions (or conversely adipose tissues).

[0353] In the “segmentation mode” embodiment, the first (or second or other in the “Black Dixon” embodiment) black blood image(s) ISNk, ISN2k used during the preliminary segmentation step SP, the positioning data of the first wall L1 and possibly those of the second wall L2 resulting from the segmentation SEG, and the data resulting from the detection steps DEG and / or characterization CAG of the adipose tissues of the heart are used. For example, the pixels or voxels of the fat image IG where the presence of adipose tissues has been detected can be identified on the first (or second or other in the “Black Dixon” embodiment) black blood image(s) ISNk, ISN2k so as to constitute a first group of pixels or voxels G.

[0354] These pixels or voxels may have the same respective positions on the fat image IG and on the first (or second or other in the “Dixon noir” embodiment) black blood image(s) ISNk, ISN2k used during the preliminary segmentation step SP, when it is considered that these images are spatially realigned and because these images have the same size and the same resolution.

[0355] Thus, the pixels or voxels identified during the preliminary segmentation step SP but not belonging to the first group of pixels or voxels G together form a second group L which can be identified as corresponding to one or more cardiac lesions. The lesion detection step DE may consist of identifying the pixels of this second group.

[0356] In Figure 7 on the left, we can observe the first black blood image ISNk of the heart section of Figure 6. In this image, the pixels or voxels identified as having an intensity greater than or equal to a predetermined intensity threshold are visible in squares only in the predetermined area delimited by the first wall L1 and / or the second wall L2.

[0357] For example, it is possible to annotate or color differently the pixels (or voxels) of the first group G and of the second group L on the first (or second or other in the “Dixon black” embodiment) black blood image(s) ISNk, ISN2k used during the preliminary segmentation step SP.

[0358] In Figure 7 on the right, we can observe an lopt image obtained from the first black blood image ISNk of the heart section of Figure 6 following such annotation, in diagonal hatching for the pixels of the first group, corresponding to the presence of adipose tissue, and in squares for the pixels of the second group, corresponding to the presence of lesions.

[0359] Lesion size calculation CAE lesion characterization advantageously includes a CTA lesion size calculation step.

[0360] This CTA calculation step comprises the determination of at least one elementary data item representative of the size of at least one cardiac lesion, for example of the myocardium, using location data of the location data of the first and / or second walls L1, L2 which are for example directly the positioning data resulting from the SEG segmentation of the myocardium or data resulting from these data, for example, data resulting from the transfer step or the positioning data of the lesion obtained during the steps of lesion detection DE and geometric characterization of lesion CAE.

[0361] In Figure 8, a first image in black blood ISNk is shown, on which the limits L1 and L2 identified during the segmentation SEG and reported, i.e. propagated, on this first image in black blood ISNk are shown in thick lines, as well as the pixels identified, during the lesion detection step DE, as being pixels of a lesion.

[0362] An elementary data representative of a lesion size can be a percentage of a surface of the myocardium occupied by a lesion on a SEC sector of a first (or second or other in the “Dixon black” embodiment) black blood image ISNk, ISN2k, ISNik or a volume or a mass of the lesion in this SEC sector starting from the axis I parallel to the axis p and passing substantially through the center of the cardiac cavity on the black blood image ISNk and delimited by two rays R starting from the axis I as visible on the first black blood image ISNk.

[0363] The percentage of the myocardial area occupied by the lesion in the SEC sector can be calculated from the ratio between the number of pixels corresponding to the lesion in this SEC sector and the number of pixels corresponding to the myocardium in this SEC sector.

[0364] The number of pixels corresponding to the lesion in this SEC sector can be calculated from the location data obtained during the lesion segmentation step or can be calculated directly, during the CTA calculation step, for example by selecting, by thresholding, the number of pixels having an intensity greater than a predetermined threshold in the portion of the SEC sector delimited by the walls L1 and L2 or by the wall L1. The CTA step can comprise the calculation of data representative of the lesion size in an SEC sector from several elementary data representative of the calculated lesion size, in this SEC sector, for several first (or second or other in the “Dixon black” embodiment) black blood images ISNk, ISN2k distributed along the axis p.

[0365] For example, we calculate a combination or an average of elementary data.

[0366] The lesion volume on an SEC sector can be calculated from the ratio between the number of pixels corresponding to the lesion on this sector and the number of pixels corresponding to the myocardium on this sector, from the thickness of the slice corresponding to a first (or second or other in the “Dixon black” embodiment) black blood image ISNk, ISN2k, when the image is two-dimensional.

[0367] The size and / or volume are advantageously also calculated from the predetermined resolution of the images.

[0368] It should be noted that the density of the myocardium is 1.06 g / ml. We therefore consider that the mass of a lesion is approximately equal to the volume of the latter, which makes it possible to evaluate the mass of the lesion.

[0369] The CTA calculation step may, for example, comprise dividing the first (or second or other in the “Dixon black” embodiment) black blood image ISNk, ISN2k into a first predefined number, equal to 12 in the non-limiting example of FIG. 1, predefined number of SEC sectors of the same angle al k opening pointing towards axis I and the calculation of the percentage of myocardial surfaces occupied by the lesion on the different SEC sectors.

[0370] The first number of sectors and the opening angle al k may 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 al k increase.

[0371] This step can be implemented for different first (or second or other in the “Dixon black” embodiment) black blood images ISNk, ISN2k of different sections centered on respective section planes PCk with k = 1 to K. The method advantageously comprises a step of generation GENR, by computer, for example by the processing unit, of a set of at least one representation of geometric lesion and / or fat characterization data and a step of display AFFD of at least one representation of the set of at least one representation on a screen of the human-machine interface.

[0372] The generation step comprises, for example, the generation of data representative of the result of the DE lesion detection step and / or the DEG detection step of adipose tissues of the heart, i.e. the absence or presence of lesion and / or adipose tissues of the heart and the display step comprises the display of this data.

[0373] As mentioned previously, the lopt image in Figure 7 on the right was obtained from the first black blood image ISNk of the heart section in Figure 6 following annotation, in diagonal hatching for the pixels of a first group of pixels G, corresponding to the presence of adipose tissue, and in squares for the pixels of a second group L, corresponding to the presence of lesions.

[0374] In another example, the at least one representation of geometric lesion and / or fat characterization data comprises a fusion of the data of the first (or second) reference white blood image, the first reference black blood image, and an image called a thresholded fat image resulting from a segmentation of the fat image. By fusion is meant a superposition of these images.

[0375] In another example, the set of at least one representation advantageously comprises a first REPT representation of the data representative of the lesion size calculated during the CTA calculation step.

[0376] For example, it is possible to generate, as visible in Figure 8, a bull's eye type representation (also called "Bull's eye" in English terminology) of the percentages or data representative of the percentages of the surfaces of the myocardium occupied by the lesion in different sectors of first (or second or other in the "Dixon black" embodiment) black blood images ISNk, ISN2k taken according to the respective cutting planes PCk.

[0377] 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.

[0378] The bull's-eye representation comprises a plurality of concentric circles C separated two by two by crowns C. Each crown CO is assigned to a slice or a set of contiguous slices, knowing that the closer the crown CO corresponds to a slice or a set of slices to the apex, the closer it is to the center of the circles. Each crown CO is divided into portions of PSE sectors in which are displayed, as in the example of figure 7, the percentages of the surface of the myocardium occupied by a lesion and calculated for the respective sectors of the first (or second or other in the "Dixon black" embodiment) black blood image ISNk, ISN2k of the corresponding slice or combinations, for example averages, of the percentages calculated from the percentages of the surface of the myocardium occupied by a lesion calculated for the sectors of the black blood images of the set of corresponding slices.

[0379] 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.

[0380] For example, in Figure 8, the display is shown in the form of a known bull's-eye representation of the respective averages of the percentages of the size of the lesion calculated in the respective sectors defined on three sets of contiguous black blood images distributed along the p axis associated with the three respective crowns corresponding respectively to a section of the apex (inner crown), to the mid-ventricle (middle crown) and to the basal zone (outer crowns).

[0381] Portions of sectors associated with a percentage greater than 80% are represented by linked dots and those associated with a percentage less than or equal to 80% are represented in white.

[0382] When generating a 3D image of the heart, it is advantageous to divide the image into several layers along the p axis and calculate the same data as from 2D images from these different layers.

[0383] Transmurality The CAE geometric lesion characterization step advantageously includes a step of calculating data representative of a percentage of transmurality of a lesion. By percentage of transmurality, we mean the percentage of a thickness of the myocardium occupied by a lesion.

[0384] This CTT calculation step includes the determination of at least one data representative of the percentage of transmurality of at least one myocardial lesion using location data of a lesion and location data of the first and / or second walls L1, L2 from the SEG segmentation step.

[0385] This data may be a percentage of the thickness of the myocardium occupied by a lesion on a sector of the first (or second or other in the “Dixon black” embodiment) black blood image ISNk, ISN2k starting from axis I.

[0386] The percentage of the thickness of the myocardium occupied by the lesion on the sector can be calculated from the ratio between a number of pixels corresponding to the thickness of the lesion on this sector and the number of pixels corresponding to the thickness of the myocardium on this sector. The number of pixels corresponding to the thickness of the lesion can be a number obtained from the results of the geometric characterization step of CAE lesion or be calculated, for example by thresholding, during the CTT calculation step from the positioning data of L1 and possibly L2 from the segmentation step SEG.

[0387] The number of pixels corresponding to the thickness of the lesion on a sector can be an average or a maximum of numbers of pixels, corresponding to the thickness of the lesion, calculated according to different radii of the sector.

[0388] 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.

[0389] The CTT calculation step may, for example, comprise dividing the first (or second or other in the “Dixon black” embodiment) black blood image ISNk, ISN2k into a second predefined number of sectors of the same angle a2 k opening pointing towards the center of the cardiac cavity and the calculation of the percentage of the myocardial surfaces occupied by the lesion in the different sectors.

[0390] The second number of sectors and therefore the opening angle a2 k may vary depending on the cutting plane PCk. For example, the closer the cutting plane PCk is to the apex, the more the number of sectors decreases and the opening angle a2 k increase.

[0391] Advantageously the second number is greater than the first number.

[0392] The CTT step may comprise the calculation of data representative of the transmurality in a sector from several elementary data representative of the transmurality calculated in this sector for several first (or second or other in the “Dixon black” embodiment) black blood images ISNk, ISN2k distributed along the axis p.

[0393] For example, we calculate a combination or an average of elementary data.

[0394] This step can be implemented for different first (or second or other in the “Dixon black” embodiment) black blood images ISNk, ISN2k of different sections centered on respective cutting planes PCk.

[0395] 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.

[0396] For example, a bull's-eye representation of combinations, e.g., averages, of percentages of lesion transmurality in sectors of contiguous ISNk black blood image sets taken along the respective PCk section planes can be generated.

[0397] The pixel intensity of this image advantageously, but not necessarily, represents the percentage of transmurality.

[0398] For example, in Figure 8, the display is shown as a bull's-eye REPTR representation of the averages of percentages of transmurality of the lesion calculated in the sectors defined on several sets of contiguous black blood images taken according to respective section planes distributed along the p axis.

[0399] 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.

[0400] 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.

[0401] The steps previously described for calculating lesion size and transmurality can advantageously be performed for adipose tissue in a similar manner based on the second adipose tissue location data.

[0402] Benefits

[0403] The proposed solution makes it possible to obtain images with sufficient resolution and contrast to detect and precisely characterize tissue singularities, for example lesions, in a reference area such as the heart, in a reliable and reproducible manner, and in particular to discriminate more precisely between tissue singularities of a different nature, such as lesions and adipose tissue.

[0404] For example, it allows, by separating three important pieces of information, namely the anatomy of the heart, the adipose tissues and the lesions, on three separate images, namely respectively the first (or second or other) images in white blood and the first (or second or other) images in black blood and the fat images, to implement an automatic method for characterizing the lesions. This automation allows a significant saving in time and reproducibility compared to the solutions of the prior art.

[0405] The acquisition sequence in black blood and white blood of the method according to the invention requires a relatively short acquisition time, in particular when acquiring signals to generate 2D images which involve little calculation. This advantageously makes it possible to implement the acquisition sequence in breath-hold breathing and to limit the movements of the heart between the images and therefore the corrections to be made, which makes it possible to limit the computing resources and the implementation of the method in real time. This also makes it possible to limit the artifacts altering the readability of the images. These artifacts accentuate the difficulty of reconstructing clear and precise images in order to locate and detect the lesion. Furthermore, long MRI acquisitions are uncomfortable for the patient. A duration of 10 to 20 min is considered a very long duration and it is difficult for the patient to remain within the MRI without making any movement.

[0406] Furthermore, in the case of the generation of 2D images by the method according to the invention, artifacts of an image extracted from a section plane of the 3D image are avoided, which are likely to lead to cases in which it is impossible to discriminate the presence of a potential lesion from the presence of blood located near the muscle. Indeed, in certain cases, the lesion is so close to the blood, it is called subendocardial, that it is difficult to know, on images presenting artifacts, whether it is a lesion, blood or an artifact of the image.

[0407] In the case where the tissue singularities are lesions and adipose tissues, these can become entangled in the case of several pathologies, and it is thus interesting to observe this phenomenon, such as, for example, the presence of adipose tissues inside the myocardium, or even the development of adipose tissues around old lesions.

[0408] The device and method according to the invention thus make it possible to improve the precision of clinical observations, and in particular to base a clinical decision with little risk of diagnostic error on the presence or absence of a lesion.

[0409] Material

[0410] From a hardware point of view, the TC processing unit can be seen as a calculator interacting with computer programs.

[0411] The TC processing unit comprises at least one computer, for example, a microcomputer, a computer network, an electronic component, a tablet, a smartphone or a personal digital assistant (PDA). The TC processing and control unit comprises, for example, a calculator, comprising a set of at least one processor, and possibly a memory operatively coupled to the calculator.

[0412] The memory comprises, for example, a computer-readable medium. The computer-readable medium is a tangible device readable by a reader of the processing unit, capable of storing electronic instructions and of being coupled to the communication system CO1, CO2.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] Alternatively, program instructions are obtained from an external source and downloaded over a network. This is particularly the case for applications.

[0417] The processing and control unit comprises a computer, 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.

[0418] The TC processing unit comprises, for example, memories, for storing data, for example the black blood and white blood images, operatively coupled to the data processing circuit and a reader adapted to read a computer-readable medium.

[0419] The steps of the method according to the invention are, for example, carried out by causing the processing circuits of the processing unit TC to read predetermined programs recorded on hardware such as memories so that their data processing circuits perform calculations, control communications and read and / or write data in memories.

[0420] The characterization 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).

[0421] The processing unit TC comprises at least one computer comprising at least elements listed below: a set of one or more processors (for example at least one central processing unit (CPU) and / or at least one graphics processing unit (GPU) and / or at least one microcontroller and / or at least one digital signal processor (DSP)) ASIC capable of interpreting instructions in the form of a computer program and / or a hardware assembly such as an application-specific integrated circuit (ASIC), an in-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.

[0422] 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.

[0423] The program product may comprise the computer-readable recording medium. The invention also relates to a computer-readable medium, on which the computer program or computer program product is recorded.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] For example, the at least one communication device comprises at least one input interface configured to receive external data, such as the first black blood signals, the first white blood signals, the second signals of the type taken from black blood and white blood, the plurality of N first black blood signals by black blood magnetic resonance and the plurality of N first white blood signals by white blood magnetic resonance. These external data can be processed by the processing unit TC during a processing step implemented by the processing unit TC as described in the present application.

[0428] For example, the at least one communication device comprises at least one output interface configured to return data resulting from calculations or from a processing step implemented by the processing unit TC as described in the present application.

[0429] The at least one communication device may comprise any hardware, firmware and / or software suitable for communicating information between elements of the device to which the communication device belongs, for example via a data bus, or to an element external to the device. In order to enable data communication between different devices to which, where appropriate, communication devices belong, these devices comprise firmware and / or software hardware enabling a wired or wireless communication link, for example Wi-Fi, Bluetooth, cellular or Ethernet, to be established between them.

[0430] 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.

[0431] The INT user interface includes, for example, an INTS interface and output and an INTE input interface.

[0432] 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.

[0433] The output interface INTS 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. The display step AFFD may be a step of returning information by a means other than a display.

[0434] The INTS output interface can be the INTE input device, for example, in the case of a touchscreen tablet.

Claims

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

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

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

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

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

6. Device according to one of claims 3 to 5 in that they depend on claim 2, in which the at least one processing unit (TC) is configured to: o generate a second fused image from the first fused image and the second adipose tissue location data, in which the lesions of the heart are differentiated from the adipose tissue.

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

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

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

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

11. Magnetic resonance imaging system according to the preceding claim, in which: o the first signals make it possible to generate a first black blood image from the first black blood signals, o the second signals make it possible to generate a first white blood image, o the second signals of said type make it possible to generate a second image of said type.

12. Magnetic resonance imaging system according to the preceding claim, in which the acquisition step comprises: o receiving, for a number N greater than 1 of interbeat pairs, a plurality of N first black blood magnetic resonance signals in black blood and a plurality of N first white blood magnetic resonance signals in white blood.

13. A magnetic resonance imaging system according to any one of claims 10 to 12, the system comprising an imaging device according to any one of claims 1 to 9.

14. Processing device comprising: o at least one input interface configured: ■ receive a first black blood image (BBI) generated from first black blood signals acquired during a pair of interbeats comprising two consecutive interbeats, by black blood magnetic resonance with late gadolinium enhancement at a first echo duration, ■ receive a first white blood image (WBI) generated from first white blood signals acquired during the inter-beat pair, by white blood magnetic resonance with late gadolinium enhancement at the first echo time, ■ receiving a second image of a type among black blood and white blood generated from the second signals of said type acquired by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration. o at least one processing unit configured to implement by computer a processing step comprising: ■ determination (DET), from the first black blood image (ISN), from the second image of said type and possibly from the first white blood image (ISB), of first data for locating a myocardial lesion and second data for locating adipose tissue, o at least one output interface configured to return said first data for locating a myocardial lesion and said second data for locating adipose tissue.

15. Device according to the preceding claim, wherein the at least one input interface is configured to: o receive a fat image generated from a first and a second phase image and from a first and a second magnitude image, o the first phase image and the first magnitude image being generated from a first reference image from the first image of said type, o the second phase image and the second magnitude image being generated from a second reference image from the second image of said type, and wherein the processing step comprises: determining, from the fat image, said second adipose tissue location data.

16. Processing device according to any one of claims 14 to 15, wherein the at least one processing unit is configured to: o when determining said first heart lesion location data and said second adipose tissue location data, segment a first reference image from the first white blood image, so as to generate positioning data for a set of at least one wall delimiting the myocardium, o implement a step of geometric lesion and / or fatty characterization of the heart, from said positioning data and said second adipose tissue location data.

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

18. Method for acquiring signals for cardiac imaging of a patient's heart, the heart comprising a myocardium delimiting a cavity of the heart, the method comprising an acquisition step comprising for at least one pair of interbeats comprising two consecutive interbeats: o during the pair of interbeats, acquisition of first signals in black blood by magnetic resonance in black blood by late gadolinium enhancement at a first echo duration, o during the pair of interbeats, acquisition of first signals in white blood by magnetic resonance in white blood by late gadolinium enhancement at the first echo duration, o during the inter-beat pair, acquisition of second signals of a type taken from black blood and white blood by magnetic resonance by late gadolinium enhancement at at least one other echo duration different from the first echo duration.

19. Acquisition method according to the preceding claim in which: o the first signals make it possible to generate a first black blood image from the first black blood signals, o the second signals make it possible to generate a first white blood image, o the second signals of said type make it possible to generate a second image of said type 20. Imaging method for cardiac imaging of a patient's heart comprising the acquisition method according to any one of claims 18 to 19, the imaging method further comprising a processing step comprising: o generation of a first black blood image from the first black blood signals, o generation of a first white blood image from the first white blood signals, o generation of a second image of said type from the second signals of said type.

21. Imaging method according to the preceding claim wherein the processing step comprises: o generating a first phase image and a first magnitude image from a first reference image from the first image of said type, o generating a second phase image and a second magnitude image from a second reference image from the second image of said type, o generate a fat image from the first and second phase images and the first and second magnitude images.

22. Processing method comprising: o receiving a first black blood image (BBI) generated from first black blood signals acquired during a pair of interbeats comprising two consecutive interbeats, by black blood magnetic resonance by late gadolinium enhancement at a first echo duration, o receiving a first white blood image (WBI) generated from first white blood signals acquired during the pair of interbeats, by white blood magnetic resonance by late gadolinium enhancement at the first echo duration, o receiving a second image of a type among black blood and white blood generated from the second signals of said type acquired by late gadolinium enhancement magnetic resonance at at least one other echo duration different from the first echo duration.o determine (DET), from the first black blood image (BBI), the second image of said type and possibly the first white blood image (WBI), first data for locating a myocardial lesion and second data for locating adipose tissue, o possibly return said first data for locating a myocardial lesion and said second data for locating adipose tissue.

23. Processing method according to the preceding claim, comprising: o receiving a fat image generated from a first and a second phase images and from a first and a second magnitude images, o the first phase image and the first magnitude image being generated from a first reference image from the first image of said type, o the second phase image and the second magnitude image being generated from a second reference image originating from the second image of said type, o determining, from the fat image, said second adipose tissue location data.

24. Treatment method according to any one of claims 22 to 24. 23, comprising: o when determining said first heart lesion location data and said second adipose tissue location data, segmenting a first reference image from the first white blood image, so as to generate positioning data for a set of at least one wall delimiting the myocardium, o implementing a step of geometric lesion and / or fat characterization (CAR) of the heart, from said positioning data and said second adipose tissue location data.

25. Treatment method according to any one of claims 22 to 25. 24, comprising displaying a first representation of the first myocardial lesion location data in a first color and a second representation of the second adipose tissue location data in a second color different from the first color.

26. Computer program product comprising instructions which, when the program is executed, cause it to implement the steps of the method according to any one of claims 22 to 25.

27. Computer-readable medium on which the computer program according to claim 26 is recorded.

Images