Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
  • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0033—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room
  • A61B5/0035—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0033—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room
  • A61B5/004—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
  • A61B5/0044—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part for the heart
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R33/00—Arrangements or instruments for measuring magnetic variables
  • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
  • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
  • G01R33/48—NMR imaging systems
  • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
  • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
  • G01R33/5601—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R33/00—Arrangements or instruments for measuring magnetic variables
  • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
  • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
  • G01R33/48—NMR imaging systems
  • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
  • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
  • G01R33/5607—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by reducing the NMR signal of a particular spin species, e.g. of a chemical species for fat suppression, or of a moving spin species for black-blood imaging
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R33/00—Arrangements or instruments for measuring magnetic variables
  • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
  • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
  • G01R33/48—NMR imaging systems
  • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
  • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
  • G01R33/5608—Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H30/00—ICT specially adapted for the handling or processing of medical images
  • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H30/00—ICT specially adapted for the handling or processing of medical images
  • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2576/00—Medical imaging apparatus involving image processing or analysis
  • A61B2576/02—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part
  • A61B2576/023—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part for the heart
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7235—Details of waveform analysis
  • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R33/00—Arrangements or instruments for measuring magnetic variables
  • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
  • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
  • G01R33/48—NMR imaging systems
  • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
  • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
  • G01R33/565—Correction of image distortions, e.g. due to magnetic field inhomogeneities
  • G01R33/56509—Correction of image distortions, e.g. due to magnetic field inhomogeneities due to motion, displacement or flow, e.g. gradient moment nulling
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R33/00—Arrangements or instruments for measuring magnetic variables
  • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
  • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
  • G01R33/48—NMR imaging systems
  • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
  • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
  • G01R33/567—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution gated by physiological signals, i.e. synchronization of acquired MR data with periodical motion of an object of interest, e.g. monitoring or triggering system for cardiac or respiratory gating

Definitions

• the invention relates to the field of cardiac magnetic resonance imaging (MRI) by late gadolinium enhancement or LGE, with reference to the Anglo-Saxon expression “Late Gadolinium Enhancement”.
• the field of application of the invention relates more particularly to methods and systems for characterizing lesions of the heart. This characterization makes it possible, in particular, to guide ablations.
• the reference technique for the characterization of regional lesions including myocardial fibrosis is imaging by late gadolinium enhancement in white blood or BR-LGE (for the Anglo-Saxon expression “bright-blood LGE”) by inversion recovery such as the PSIR sequence (from the Anglo-Saxon expression “phase-sensitive inversion-recovery”).
• inversion recovery such as the PSIR sequence (from the Anglo-Saxon expression “phase-sensitive inversion-recovery”).
• the cancellation of the viable myocardial signal is caused using inversion-recovery pulses, which allows lesions to be visualized with high contrast between healthy myocardial tissue and the lesions.
• black blood LGE (BL-LGE) imaging techniques have been proposed. They make it possible to simultaneously cancel the signals from the healthy myocardium and the blood, thus providing high contrast both between the lesions and between the blood and between the lesions and the healthy myocardium.
• black blood imaging techniques do not make it possible to correctly characterize the lesions, in particular to locate them precisely in relation to the myocardium, as the contrast between the blood and the healthy myocardium is not sufficiently high.
• the characterization of scars is currently carried out by radiologists by manually segmenting the scars. lesions from images from PSIR sequences, which then makes it possible to calculate their transmurality and their size.
• this process is time-consuming since 25 to 30 minutes are necessary for the radiologist to carry out such segmentation.
• segmentation is imprecise and not reproducible. Indeed, the low contrast between the lesion and the blood often leads the radiologist to imagine the subendocardial wall, which leads to an overestimation or underestimation of the characteristics of the scar.
• An aim of the invention is to limit at least one of the aforementioned drawbacks.
• the subject of the invention is a method for characterizing lesions of the heart using images of an area to be imaged comprising the heart of a patient, the heart comprising a myocardium delimiting a cavity of the heart, the images comprising:
• the segmentation uses a learning function to segment the white blood image so as to obtain the positioning data.
• the learning function is a convolutional neural network.
• the 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 magnetic resonance in white blood by late enhancement of the distinct gadolinium inversion sequences recovery.
• the assembly of at least one wall comprises a first wall delimiting and surrounding the myocardium.
• the characterization includes lesion segmentation to locate a myocardial lesion on the black blood image using data from positioning data of the first wall.
• the lesion segmentation comprises the selection of pixels of the black blood image having an intensity greater than a predetermined threshold, the pixels being taken only from the pixels of the black blood image surrounded by the first wall.
• the assembly of at least one wall comprises a second wall delimiting the myocardium and surrounded by the first wall.
• the characterization comprises the calculation of data representative of a lesion size from data resulting from the positioning data of the first wall and possibly a second wall surrounded by the first wall.
• the characterization comprises the calculation of data representative of a percentage of transmurality of the lesion from data resulting from the positioning data of the first wall and the second wall.
• the method comprises the display, on a screen, of a representation of data calculated during the characterization step.
• the process comprises:
• black blood acquisition - generation of the image in white blood from the signals acquired during the magnetic resonance acquisition in black blood by late gadolinium enhancement, called black blood acquisition.
• the black blood acquisition and the white blood acquisition belong to an acquisition sequence comprising an elementary acquisition sequence each comprising a white blood acquisition and a black blood acquisition.
• the black blood acquisition and the white blood acquisition are implemented during a pair of interbeats consisting of two consecutive interbeats.
• the elementary acquisition sequences are implemented during pairs of consecutive respective interbeats.
• the method comprises lesion segmentation and characterization from black blood and white blood images generated from white blood images and black blood images generated from signals acquired during elementary sequences of the sequence of acquisition.
• the method includes the acquisition sequence.
• the method comprises the generation of the black blood image and the white blood image.
• the black blood image and the white blood image are two-dimensional.
• the invention also relates to a system comprising the hardware and software elements for implementing the method according to the invention.
• the system advantageously comprises a processing unit configured to implement the segmentation step and the lesion characterization step.
• the processing unit is a processing unit.
• 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.
• the processing unit is configured to generate commands intended for the MRI device so that it implements acquisition by magnetic resonance in black blood and acquisition by magnetic resonance in white blood.
• the processing unit is configured to generate white blood and black blood images from the signals acquired during the respective acquisitions.
• the system comprises an electrocardiograph configured to acquire an electrocardiogram of the patient during the respective acquisitions.
• the invention also relates to a computer program product comprising instructions which cause the system according to the invention to execute the steps of the method according to the invention.
• the invention also relates to a computer-readable medium, on which the computer program according to the invention is recorded.
• FIG. 1 an example of producing a system according to the invention
• FIG. 2 a schematic representation of an elementary signal acquisition sequence for generating black blood images and white blood images used in the method according to the invention
• FIG. 3 a schematic representation of an MRI acquisition phase in black blood and white blood carried out on a plurality of heart beats
• FIG. 4 a schematic representation of a heart in three dimensions (3D) illustrating different section planes distributed along the long axis of the heart and images generated from signals acquired in one of the section planes
• FIG. 5 a flowchart of an example of a process according to the invention
• FIG. 6 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 step of segmentation, and at the bottom left an image in black blood and the representation of the walls transferred to the image in black blood,
• FIG. 7 at the top, the bottom left image of Figure 6 on which sectors have been represented, at the bottom left a bull's eye type representation of the lesion size and at the bottom right an eye type representation of beef with a lesional percentage of transmurality.
• the invention relates to the field of cardiac imaging by magnetic resonance or MRI, late enhancement of gadolinium in black blood and white blood.
• the invention relates to a method for characterizing cardiac lesions, and more precisely of at least one cardiac muscle, for example the myocardium.
• Cardiac injury means injury to a muscle of the myocardium.
• Cardiac lesions can be divided into acute lesions following acute myocardial damage, such as acute myocardial infarction, and chronic lesions characteristic of chronic cardiac pathologies. These lesions are cardiac lesions, for example of the myocardium or papillary muscles. These lesions include myocardial fibrosis which frequently develops in the context of hypertrophic or dilated cardiomyopathy, but which also represents a frequent sequela of inflammatory heart disease or myocardial infarction.
• the lesions also include myocardial necrosis, that is to say the volumes of myocytes whose cell membrane has been destroyed and the volumes of extracellular matrices and collagen constituting the fibrous scars, in the chronic phase of the infarction.
• FIG. 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.
• this system S comprises a set of measuring equipment A comprising a magnetic resonance imaging (MRI) device B as well as an electrocardiograph referenced ECR in FIG. 1.
• MRI magnetic resonance imaging
• ECR electrocardiograph referenced
• the system S also includes a processing device C comprising a processing unit TC and a man-machine interface INT.
• This processing device can 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.
• the MRI imaging device B comprises a static magnetic field generator GEN_B, a gradient generator GEN_GRAD and a radio frequency (RF) device D_RF.
• GEN_B static magnetic field generator
• GEN_GRAD gradient generator
• RF radio frequency
• 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 include the area of the patient to be imaged , this area to be imaged including the heart.
• a polarization zone generally a tunnel
• the patient is a mammal.
• the mammal is a man.
• the GEN_GRAD gradient generator comprises three gradient coils (or solenoids) arranged and configured to vary the intensity of the magnetic field in the polarization zone according to the respective orthogonal x, y and z axes fixed with respect to the polarization zone.
• the choice of intensities circulating in these coils makes it possible to select, among several possibilities, a section, presenting a given thickness and a section plane on which the section is centered, in which the magnetization of the patient's area to be imaged will be measured. received in the polarization zone.
• the D_RF radiofrequency device comprises coils or solenoids and is capable of generating MRI acquisition sequences including preparatory sequences for the magnetization of the area to be imaged and reading sequences of RF signals from the area to be imaged.
• Each of the preparatory and reading sequences comprises at least one radio frequency pulse of predetermined and adjustable frequency, shape, duration, phase, and amplitude.
• the preparatory sequence is configured to excite, that is to say modify the direction of the magnetization of the tissues in the area to be imaged.
• the reading sequence is configured to measure the magnetization of the area to be imaged resulting from the preparatory module.
• the ECR electrocardiograph is intended to acquire an electrocardiogram of the patient
• the processing unit TC is configured to generate commands intended for the IRM device B, in particular intended for the RF device D_RF and the gradient generator GEN_GRAD, so that the IRM device generates the predefined acquisition sequences of signals from predefined volumes or sections of the area to be imaged.
• the TC processing unit is also configured to generate images of the area to be imaged from the measured signals, using 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.
• Figure 2 represents an example of an elementary acquisition sequence SE1 of an MRI acquisition sequence of RF signals making it possible to generate images of the heart as well as an electrocardiogram (ECG) E measured by the electrocardiograph ECR during the sequence elementary SE1.
• ECG electrocardiogram
• 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.
• the elementary acquisition sequence SE1 includes an acquisition called black blood ACQ1 followed by an acquisition called white blood ACQ2 which will be described later.
• Acquisition in black blood ACQ1 makes it possible to acquire the signals of the area to be imaged making it possible to generate an elementary image in black blood IM1 of the area to be imaged.
• Acquisition in white blood ACQ2 makes it possible to acquire the signals of the area to be imaged, making it possible to generate an elementary image in white blood IM2 of the area to be imaged.
• These Acquisition stages ACQ1, ACQ2 each include a preparatory module PREP1, PREP2 and a reading module LE1, LE2.
• module means a step comprising a radio frequency pulse or a series of radio frequency pulses.
• 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.
• the gradient generator GEN_GRAD is for its part controlled for the processing unit TC so that the radio frequency device D_RF acquires signals coming from a predefined section having a predefined thickness during the elementary acquisition sequence SE1.
• the acquisition sequence is an acquisition sequence by late gadolinium enhancement implemented following the injection of a Gadolinium-based contrast product intravenously into the patient, 10 to 15 minutes before the start-up. implements acquisition sequences so as to obtain images with maximum contrast between lesions and healthy tissues and blood.
• a Gadolinium-based contrast product intravenously into the patient, 10 to 15 minutes before the start-up.
• contrast is rapidly eliminated from healthy myocardium, poor in interstitial tissue, but accumulates over a prolonged period in myocardial lesions.
• Gadolinium has an extracellular distribution, that is to say it does not cross the membranes of cardiomyocytes.
• 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 blood and healthy myocardium.
• the RF device D_RF implements an acquisition step in black blood ACQ1 in inversion-recovery.
• This ACQ1 black blood acquisition step includes a longitudinal inversion pulse denoted 180° in Figure 2, which tilts the longitudinal magnetization of the tissues of the imaged area in the opposite direction, that is to say which inverts the longitudinal magnetization of these tissues.
• the magnetization of the area to be imaged goes from Mz to -Mz under the effect of the inversion pulse. Due to longitudinal relaxation, the longitudinal magnetization of the different tissues present in the area to be imaged increases to return to its initial value, passing through the zero value. Naturally, the relaxation kinetics of different tissues are different.
• the black blood acquisition ACQ1 also comprises a preparatory module PREP1 implemented after the 180° longitudinal inversion pulse, for example, an adiabatic module in T1 -rho (Tip) of duration denoted TSL (acronym of the Anglo-Saxon expression “Time of Spin Lock”) or a T2-weighted module, or MTC type (acronym of the Anglo-Saxon expression “Magnetization Transfer Contrast”) or a combination of two of these modules or these three modules.
• a preparatory module PREP1 implemented after the 180° longitudinal inversion pulse, for example, an adiabatic module in T1 -rho (Tip) of duration denoted TSL (acronym of the Anglo-Saxon expression “Time of Spin Lock”) or a T2-weighted module, or MTC type (acronym of the Anglo-Saxon expression “Magnetization Transfer Contrast”) or a combination of two of these modules or 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 t.
• the longitudinal magnetization of the A(Cica) lesions is significantly greater than zero.
• the first acquisition step ACQ1 in inversion-recovery then comprises a reading sequence LE1 comprising a 90° pulse applied at time te and a reading gradient to read the transverse magnetization of the area to be imaged.
• the inversion time Tl is the duration separating the 180° pulse of the reading sequence LE1 from the elementary sequence ACQ1.
• the reading module LE1 of the black blood acquisition is spaced temporally from the preparatory module PREP1 of the black blood acquisition.
• the LE1 reading module begins as soon as the PREP1 preparatory module ends. The same applies to the relative temporal positioning between the PREP2 preparatory module of the white blood acquisition and the LE2 reading module of the white blood acquisition.
• the inversion pulse IMP1 is generated before the preparatory module PREP1.
• the preparatory module PREP1 is generated before the inversion pulse IMP1.
• the elementary white blood image IM2 of the area to be imaged is generated from signals acquired by implementing the white blood acquisition step ACQ2 comprising a preparatory module PREP2 followed by a reading module LE2.
• 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.
• the PREP2 preparatory module is, for example, an adiabatic T1 rho sequence.
• the PREP2 module comprises 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.
• the white blood acquisition step ACQ2 then comprises a reading module LE2 comprising a reading gradient to read the transverse magnetization of the area to be imaged.
• This reading module LE2 can be produced in gradient echo or spin echo, just like the reading module LE1 of the black blood acquisition step LE1.
• the LE1 and LE2 reading modules can be the same or different.
• the duration D2 separating the reading module LE2 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 shown in Figure 2.
• the processing unit TC is configured to synchronize the acquisition sequence SE with the electrocardiogram E.
• the TC processing unit uses the electrocardiogram E to generate commands for triggering the acquisition sequences intended for the RF device, the gradient generator and possibly the generator of the main magnetic field.
• Each elementary acquisition sequence SEi is advantageously implemented during two consecutive cardiac cycles, preferably during two consecutive interbeats C1, C2 referenced in Figure 2 constituting a pair of interbeats 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.
• a beat, a QRS complex, and an interbeat a phase of a cardiac cycle located between two consecutive beats.
• the consecutive elementary acquisition sequences SEi are implemented during pairs of consecutive interbeats CBi.
• Each SEi elementary acquisition sequence includes:
• An advantage is to minimize the acquisition time and therefore to minimize the movements of the heart between the different acquisitions and the spatial shifts between the images IM 1 and IM2 acquired during the different elementary sequences SEi.
• the acquisition sequence SE and the electrocardiogram E are synchronized so that the reading modules LE1, LE2, are implemented during the same phase of respective cardiac cycles C1, C2 .
• this phase is an interbeat.
• this phase is diastole.
• 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 instants of these cardiac cycles respective C1, C2.
• the temporal reference is, for example; the maximum of the QRS complex.
• the synchronization of the reading modules LE1, LE2 of the black blood and white blood acquisition sequences ACQ1, ACQ2 and, as we will see later, of different step reading modules acquisition in white blood on the one hand and the different modules for reading acquisition stages 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 reference frame fixed relative to the main magnet which allows the images obtained to be superimposed without any registration being necessary or by carrying out a simple registration.
• the invention also applies when the order of the acquisition steps is different. For example, we can implement several ACQ1 black blood acquisition steps during consecutive interbeats then several ACQ2 white blood acquisition steps during consecutive interbeats and vice versa.
• At least one ACQ2 white blood acquisition step is spaced from a temporally closest black blood ACQ1 acquisition step.
• the TC control processing unit is configured so as to generate commands intended for the MRI device to acquire respective cut signals distributed along a predefined axis of the heart.
• the axis is advantageously the long axis of the heart.
• the cuts obtained are then so-called short axis cuts.
• An advantage is that it allows excellent visualization of both ventricles.
• 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.
• cut we mean in the present application, a slice or layer perpendicular to the axis g and having a predefined thickness along the axis.
• the cuts are contiguous along the axis.
• the signals are acquired according to adjacent or partially overlapping sections. This allows the heart to be completely imaged.
• the gradient generator GEN_GRAD is controlled so that several two-dimensional images of each section can be generated, from the signals acquired during the acquisition sequence SE.
• the cutting planes are distributed from the AP apex to the BA base of the CO heart. Consequently, the cuts are called “short axis cuts”.
• 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.
• the SE acquisition sequence is implemented while the patient is in apnea.
• the SE acquisition sequence is implemented in free breathing.
• Free-breathing acquisition has temporal advantages. Indeed, free-diving acquisition must be rapid, which requires reducing the acquisition time from a few seconds to a few minutes and often involves having to reduce the area to be imaged, for example by limiting it to a 3D portion.
• the acquisition sequence SE being synchronized with the ECG so that the reading sequences are put implemented at the same time marker of different cardiac cycles, breath-hold acquisition leads to generating a limited number of images.
• the TC processing unit is configured to generate GEN, by reconstruction techniques known to those skilled in the art, images of the area to be imaged.
• the generation step includes a step of generating elementary two-dimensional (2D) or three-dimensional (3D) images of the area to be imaged from the signals acquired during the acquisition sequence SE.
• the elementary images IM1, IM2 are advantageously generated in gray level.
• Each image comprises a set of unit 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 and N-1.
• N being a finite integer greater than 1
• 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.
• the TC processing unit is also configured to use the elementary images obtained to characterize lesions of the heart, for example of the myocardium as we will see in more detail later in the text.
• the elementary images generated by the processing unit TC can be intended to be displayed on a screen of the man-machine interface INT.
• the method does not have an image registration step.
• the step of generating GEN images comprises the registration of elementary images in black blood with each other and/or the registration of elementary images in white blood with each other and/or the registration of elementary images in black blood and in white blood between them.
• the method comprises the registration of elementary images in black blood of the same section.
• the registration is carried out using a non-rigid image registration algorithm.
• the method comprises the registration of elementary white blood images of the same section.
• the registration is carried out using a non-rigid image registration algorithm.
• the method comprises the registration of elementary images in black blood IM1 and in white blood IM2 with each other.
• the method comprises the registration of elementary images in white blood and elementary images in black blood of the same section.
• this registration is carried out using a non-rigid image registration algorithm.
• these algorithms are identical. It is possible to choose a different algorithm for processing elementary images in black blood and white blood, but it is preferable to choose the same algorithm for ease of implementation. Registration significantly improves the quality, in particular the contrast, of an image resulting from a plurality of images elementary from the same cup. Furthermore, it helps reduce artifacts related to breathing.
• 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.
• 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 to minimize the difference between two images.
• the deviation 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, singularities of shapes or contours.
• a parametric or non-parametric approach can be used.
• the least squares method can be used.
• the registration can be carried out by choosing a reference image and determining a transformation function of the other images of the same section with respect to this image. Each image is then registered by optimizing a transformation to obtain the reference image according to a geometric criterion from the image considered.
• acquiring three-dimensional images it is possible to acquire several three-dimensional images of the heart, which can possibly be registered.
• the combination of images can be carried out before, during or after the GEN generation of the images.
• the combination is an averaging.
• Averaging is, for example, carried out in image space or in Fourier space (i.e., the frequency domain, before reconstruction of the images). These solutions are computationally inexpensive and fast.
• Averaging has the advantage of preserving image detail, since it increases the signal-to-noise ratio (SNR). This technique smoothes noise to reduce residual image artifacts. Additionally, averaging helps improve the bit depth of the digital image beyond what is possible with a single image.
• SNR signal-to-noise ratio
• An advantage of the step of averaging the images produced from the same section is to reduce the maximum deviation.
• the amplitude of the noise decreases as the square root of the number of images used, i.e. with only 4 images we can reduce the amplitude of the noise by a factor two. According to an example of a free breathing acquisition lasting 2 minutes, it is possible to collect 4 to 5 images per cutting plane, which makes it possible to obtain good noise reduction performance.
• the combination of images can alternatively be implemented by motion-compensated iterative reconstruction.
• this type of combination is implemented during image reconstruction.
• Compensated MRI reconstruction techniques are described in particular in the following articles: Odille F, et al., “Generalized reconstruction by inversion of coupled systems (GRICS) applied to free-breathing MRI” Magnetic Resonance in Medicine, 2008; and “3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST”, Bustin A, et al, Journal of Cardiovascular Magnetic Resonance, 2020.
• GRICS inversion of coupled systems
• the elementary images on the one hand in black blood and on the other hand in white blood are respectively combined so as to produce a combined black blood image ISNk and a combined white blood image ISBk by section.
• these elementary images IM1, IM2 are generated from signals acquired by implementing an acquisition sequence.
• This acquisition sequence includes ACQ2 white blood acquisition steps.
• Each ACQ2 white-blood acquisition step is distinct from a reversal-recovery sequence.
• this sequence is devoid of a pulse for reversing the longitudinal magnetization of the area to be imaged. Consequently, unlike PSIR imaging during white blood acquisition, the longitudinal magnetization of the myocardium is not canceled which makes it possible to obtain images presenting a stronger contrast between the lesions and the blood and therefore promote diagnosis and image processing.
• 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 healthy myocardium and that of blood. This is obtained by the configuration of the preparatory module PREP2 and that of the reading module LE2 and by the relative temporal positioning between these two modules.
• the lesion characterization process includes the following steps:
• Each white blood ISB, respectively black blood ISN image is an elementary image IM1 k(j), IM2kO) or a combined image in white blood ISBk, respectively in black blood ISNk.
• each image in white blood ISB is a combined image in white blood ISBk and that each image in black blood ISN is a combined image in black blood ISNk .
• 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.
• segmentation of ISBk white blood images generated from signals measured during the ACQ2 white blood acquisition steps distinct from inversion-recovery sequences makes it possible to position the walls in an automatic, robust, reliable and precise manner. delineating the myocardium, as these images show significant contrast between the myocardium and blood. ISNk black blood images do not provide as good results due to the lack of contrast between the healthy myocardium and the blood.
• the lesion characterization which uses, in addition to the positioning data resulting from the segmentation, an ISNk black-blood image, makes it possible to obtain good results that would not make it possible to obtain on its own the ISBk white-blood image containing little or no no information on cardiac damage.
• the SEG segmentation is implemented using at least one white blood image ISBk 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.
• the positioning data relating to a wall corresponds, for example, to the identification of the pixels constituting the wall.
• the generated images are, for example, in gray levels.
• the white areas in Figure 6 represent areas lighter than the dotted areas represent areas lighter than the gridded areas which represent areas lighter than the bricks.
• the cavity of the heart is the left ventricle and the SEG segmentation is implemented so as to delimit the walls L1, L2 of the part of the myocardium surrounding and delimiting the left ventricle.
• the invention is described in the case where the cavity of the heart is the left ventricle, but the invention is applicable to any chamber of the heart, such as the right ventricle and atria which are also surrounded and bounded by the myocardium and subject to cardiac damage.
• the second L2 wall is the wall delimiting the myocardium and the LV left ventricle.
• the first L1 wall surrounding the second L2 wall is the external wall, that is to say facing outwards, of the LV left ventricle, of the part of the myocardium surrounding the LV left ventricle. This is the epicardium.
• the second L2 wall is the wall of the myocardium delimiting the left ventricle. This is the endocardium.
• SEG segmentation is implemented so as to generate positioning data for only one of these two walls, for example the external wall of the myocardium.
• the segmentation is carried out by implementing a learning function or algorithm, for example an artificial neural network, to segment a white blood image so as to delimit at least one wall of the myocardium surrounding and delimiting a cavity of the heart.
• a learning function or algorithm for example an artificial neural network
• the learning function is a neural network.
• the artificial neural network used for segmentation is advantageously a convolutional neural network.
• the convolutional neural network is, for example, of the U-Net type or of the transformer type also called self-attentive model, for example, of the type commonly called swin transformer.
• the neural network or more generally the learning function, is implemented on two-dimensional (2D) images and/or on three-dimensional (3D) images. In other words, it is trained to carry out the desired segmentation by receiving 2D and/or 3D images as input.
• the learning function is trained, prior to implementing the method according to the invention, from white blood images of the heart, generated from signals acquired during respective white blood acquisition stages distinct from recovery inversion sequences, and labeled by specialists, that is to say segmented by specialists, so that the trained learning function receiving input data comprising a white blood image of the heart, is able to segment so as to delimit at least one wall of the myocardium surrounding and delimiting a cavity of the heart.
• the 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 a sequence of inversion recovery, 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.
• the method may include a step of propagation of the walls detected during the SEG segmentation step, on at least one dark blood image.
• the method can include a REP transfer step, that is to say the propagation, comprising the identification, on a black blood image ISNk, of the pixels or voxels corresponding to the walls L1 and L2 identified during SEG segmentation.
• FIG 6 there is shown schematically at the bottom left a black blood image ISNk of the section of the heart and at the bottom right the black blood image on which the walls L1 and L2 detected are represented in thick black lines during the SEG segmentation step.
• the identification, on the 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 blood image white ISBk.
• pixels or voxels can have the same respective positions on the white blood image and on the black blood image when we consider that these images are spatially aligned and because these images have the same size and the same resolution.
• a predetermined or calculated spatial offset may alternatively be applied to these pixels or voxels when it is estimated that a spatial offset exists between these images.
• the report may include the annotation or coloring of pixels or voxels corresponding to walls L1 and L2.
• the characterization includes segmentation for each ISBk white blood image so as to generate respective positioning data obtained from the respective ISBk white blood images.
• the characterization includes the transfer to each 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 white blood images ISBk.
• the pixels or voxels reported on the different black blood images or combined black blood images are advantageously identified from respective white blood images.
• the positioning data used for transfer to a black blood image ISNk are generated from a white blood image ISBk generated from signals measured during the same elementary acquisition sequence.
• the positioning data generated from a combined white blood image ISBk of order k is advantageously transferred to a combined black blood image ISNk of order k.
• the CAR lesion characterization step consists of characterizing the heart from the point of view of the lesions using one or more black-blood image(s) ISNk and positioning data of at least one wall, for example of the second wall L2 , obtained from one or more ISBk white blood images.
• This step advantageously makes it possible to generate data characterizing the heart from the point of view of lesions.
• This step is implemented by the CT processing unit.
• the CAR characterization step may include a DE lesion detection step and/or a CAE lesion characterization step.
• the CAE lesion characterization step is implemented.
• the CAE lesion characterization step can be implemented only on the condition that a lesion is detected during the DE detection step.
• 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.
• the CAE lesion characterization step does not have a DE detection step.
• the detection step will be described later.
• the lesion characterization step CAE advantageously comprises a lesion segmentation step SC of at least one black blood image ISNk so as to generate positioning data of at least one cardiac lesion possibly present in the figure, the lesion segmentation SC using positioning data from the first wall L1 and possibly those from the second wall L2, generated during the SEG segmentation step.
• size of a lesion we mean data representative of the dimensions of the lesion, such as a volume or a surface, for example or a number of pixels or voxels.
• SC lesion segmentation uses one or more ISNk black blood image(s) and positioning data from the first wall L1 and possibly those of the second L2 wall resulting from SEG segmentation.
• This positioning data can be positioning data generated during the SEG segmentation step or positioning data from the REP report step.
• the SG lesion segmentation step includes the report step.
• This step makes it possible to locate cardiac lesions, that is to say, to generate data on the location of cardiac lesions.
• This lesion location data includes, for example, the identification or positions of pixels or voxels corresponding to lesions.
• the SC lesion segmentation step is advantageously implemented by thresholding.
• This zone is determined from the positioning data of the first wall L1 and possibly those of the second wall L2 resulting from the SEG segmentation.
• the area of the black blood image is the area surrounded and delimited by the first wall L1.
• the lesion segmentation step SC comprises the search 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 image(s). in black blood ISNk.
• these pixels or voxels are taken only from the pixels or voxels of an area of the image or images in black blood surrounded and delimited by the first wall L1. This helps prevent erroneous detection of lesions by beyond the epicardium, avoiding confusion between lesions and fat surrounding the epicardium and represented, on black blood images, by high intensity pixels.
• the zone Z is the zone of the black blood image(s) ISNk delimited by the first wall L1 and by the second wall L2.
• SC lesion segmentation includes the search for pixels of 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 black blood images ISNk.
• This variant has the advantage of identifying pixels or voxels of myocardial lesions 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.
• the SC lesion segmentation includes the search for pixels of 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.
• the segmentation is implemented using 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 data as input.
• 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 data as input.
• the DE detection step comprises detecting the absence or presence of lesions using a black-blood image ISNk and positioning data of at least one wall, for example, of the second wall L2. It generates an output indication of the presence or absence of cardiac lesions.
• the DE detection step can be carried out by thresholding or by using a neural network like the segmentation step.
• This step may consist of determining whether a number of contiguous pixels or voxels greater than a predetermined threshold has an intensity greater than a predetermined threshold in the area delimited by the wall L1 and/or the wall L2 whose positioning is defined during the SEG segmentation step of the myocardium. The presence of cardiac lesions is detected if this condition is verified and the absence of cardiac lesions 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 walls L1 and/or L2 when it receives as input the positioning data of the corresponding wall(s) and the image in black blood.
• the next step is advantageously the GENR generation step.
• the black blood image ISNk is shown, on which the limits L1 and L2 identified during the segmentation and transferred, that is to say propagated, on the image are represented in thick lines. in black blood ISNk as well as the pixels identified as being pixels of the lesion.
• the CAR characterization advantageously includes a CTA calculation step of lesion size.
• This CTA calculation step comprises the determination of at least one elementary data 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 positioning data of the lesion obtained during the SC lesion segmentation step.
• 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 black blood image ISNk or a volume or a mass of the lesion in this SEC sector starting from axis I parallel to 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 axis I as visible on the black blood image ISNk.
• the percentage of the surface of the myocardium 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.
• 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 directly be calculated 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 may include the calculation of data representative of the lesion size in a SEC sector from several elementary data representative of the lesion size calculated, in this SEC sector, for several black blood images ISNk distributed along the p axis.
• a combination or an average of elementary data is calculated.
• the volume of lesion on a SEC sector can be calculated from the ratio between the number of pixels corresponding to the lesion on this sector and the number of pixels corresponding to the myocardium on this sector, from the thickness of the section corresponding to a black blood image ISNk, when the image is two-dimensional.
• the size and/or volume are advantageously also calculated from the predetermined resolution of the images.
• 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.
• the CTA calculation step can, for example, include the division of the black blood image ISNk into a first predefined number, equal to 12 in the non-limiting example of Figure 1, predefined SEC sectors of the same angle crl opening fc pointing towards axis I and the calculation of the percentage of myocardial surfaces occupied by the lesion on the different SEC sectors.
• the first number of sectors and the opening angle al k can vary depending on the cutting plane PCk. For example, the closer the cutting plane PCk gets to the apex along the major axis, the more the number of sectors decreases and the opening angle al k increases.
• the method advantageously comprises a GENR generation step, by computer, for example by processing unit, a set of at least representation of lesion characterization data and an AFFD display step of at least one representation of the set of at least one representation on a screen of the human interface - machine.
• the generation step comprises for example the generation of data representative of the result of the detection step DE, that is to say the absence or presence of lesion and the display step comprises displaying this data.
• 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.
• the Bull's eye representation is defined by the American Heart Association AHA with reference to the Anglo-Saxon expression "American Heart Association” and described in the following article: "Standardized Myocardial Segmentation and Nomenclature for Tomography Imaging of the Heart: A Statement for Healthcare Professionals From the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. » Manuel D. Cerqueira et Al, Circulation, 2002; 105:539-42.
• the bull's eye type representation includes a plurality of concentric circles CE separated in pairs by crowns CO.
• Each CO crown is assigned to a cut or set of cuts contiguous knowing that the more the crown CO corresponds to a cut or a set of cuts close to the apex, the closer it is to the center of the circles.
• Each crown CO is divided into portions of sectors PSE 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 black blood image ISNk of the corresponding section or combinations, for example averages, 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 sections.
• the intensity of the pixels of the different portions of crowns depends on the calculated percentage. The lower this percentage, the higher the intensity of the corresponding corona.
• the display is shown in the form of a known bull's eye type representation of the respective averages of the percentages of the size of the lesion calculated in the respective sectors defined on three sets of images in contiguous black blood 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).
• the CAR characterization step advantageously includes a step of calculating data representative of a percentage of transmurality of a lesion.
• percentage of transmurality we mean the percentage of a thickness of the myocardium occupied by a lesion.
• This CTT calculation step comprises the determination of at least one data representative of the percentage of transmurality of at least one myocardial lesion using lesion location data and location data of the first and/or second walls L1, L2 from the segmentation step.
• This data can be a percentage of the thickness of the myocardium occupied by a lesion on a sector of the black blood image ISNk starting from axis I
• 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 in this sector.
• the number of pixels corresponding to the thickness of the lesion can be a number obtained from the results of the SC lesion segmentation step or be calculated, for example by thresholding, during the CTT calculation step from the data. positioning of L1 and possibly L2 from the SEG segmentation step.
• 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.
• the number of pixels corresponding to the thickness of the myocardium on a sector can be an average or a maximum of numbers of pixels, corresponding to the thickness of the myocardium, calculated according to different radii of the sector. These numbers are calculated from positioning data of walls L1 and L2.
• the CTT calculation step can, for example, include the division of the black blood image ISNk into a second predefined number of sectors with the same opening angle a2 k pointing towards the center of the cardiac cavity and the calculation of the percentage of surfaces of the myocardium occupied by the lesion on the different sectors.
• the second number of sectors and therefore the opening angle a2 k can vary depending on the cutting plane PCk. For example, the closer the cutting plane PCk gets to the apex, the more the number of sectors decreases and the opening angle a2 k increases.
• the CTT step may include 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 black blood images ISNk distributed along the p axis.
• a combination or an average of elementary data is calculated.
• This step can be implemented for different black blood images ISNk of different sections centered on respective PCk section planes.
• the GENR step advantageously comprises the generation of a REPTR representation of data representative of a percentage of transmurality.
• the AFFD display step advantageously includes the display of this representation.
• the intensity of the pixels in this image advantageously, but not necessarily, represents the percentage of transmurality.
• the display is shown in the form of a bull's eye type REPTR representation of the averages of percentages of transmurality of the lesion calculated in the sectors defined on several sets of black blood images. contiguous taken according to respective cutting planes distributed along the axis p.
• the bull's eye type representation includes a plurality of portions of sectors whose intensity corresponds to the combination of the percentage of transmurality calculated for this portion of sector.
• the percentages are displayed in the sector portions.
• 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 signals are acquired to generate 2D images which involve little calculation.
• This advantageously makes it possible to implement the acquisition sequence during apnea 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 calculation resources and the implementation of the method in time. real.
• 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.
• artifacts of an image extracted from a cutting plane of the 3D image likely to result in cases in which it is impossible to discriminate the presence of a potential lesion from the presence of blood located near the muscle.
• the lesion is so close to the blood, we call it subendocardial, that it is difficult to know, on images presenting artifacts, if it is a lesion, blood or an artifact of the image.
• the method according to the invention makes it possible to base a clinical decision with little risk of diagnostic error on the presence of a lesion or not.
• the TC processing unit can be seen as a calculator interacting with computer programs.
• 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).
• a microcomputer for example, a microcomputer, a computer network, an electronic component, a tablet, a Smartphone or a personal digital assistant (PDA).
• PDA personal digital assistant
• the TC processing and control unit comprises, for example, a calculator, comprising a set of at least one processor, and possibly a memory operationally coupled to the calculator.
• the memory includes, for example, a computer-readable medium.
• the computer-readable medium is a tangible device readable by a reader of the processing unit, capable of memorizing electronic instructions and of being coupled to the communication system or communication unit.
• computer-readable media is tangible media. That is, it is not a transient signal per se, such as radio waves or other freely propagating electromagnetic waves, such as light pulses or electronic signals.
• 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. -this.
• the readable medium is an optical disk, a magneto-optical disk, a read-only memory (ROM, from English Read-Only Memory), an erasable and programmable read-only memory (EPROM, from English Erasable Programmable Read-Only Memory), a programmable and electrically erasable read-only memory (ÉEPROM, from English Electrically Erasable Programmable Read-Only Memory), a random access memory (RAM, from English Random Access Memory), a magnetic card or a optical card.
• ROM read-only memory
• EPROM erasable and programmable read-only memory
• EUEPROM programmable and electrically erasable read-only memory
• RAM random access memory
• magnetic card or a optical card.
• 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.
• program instructions are taken from an external source and downloaded over a network. This is particularly the case for applications.
• the processing and control unit comprises a calculator, that is to say at least one electronic data processing circuit designed to manipulate and/or transform data represented by electronic or physical quantities in registers of the system evaluation 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.
• the CT processing unit comprises, for example, memories, for storing data, for example black blood and white blood images, operationally coupled to the data processing circuit and a reader adapted to read a computer readable medium.
• the steps of the method according to the invention are, for example, executed by causing the processing circuits of the CT processing unit to read predetermined programs recorded on hardware such as memories such that their data processing circuits perform calculations, control communications and read and/or write data to memories.
• the characterization is, for example, carried out on a processing device, for example a single computer, or on a system distributed between several computers (in particular via the use of cloud computing).
• the CT processing unit comprises at least one calculator 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 processing unit graphics processing (GPU) and/or a microcontroller and/or a digital signal processor (DSP)) ASIC capable of interpreting instructions in the form of a computer program and/or a hardware assembly such as an integrated circuit specific to an application ( ASIC), an in situ programmable gate array (FPGA), a programmable logic device (PLD), 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.
• 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 .
• the program product may include the computer-readable recording medium.
• the invention also relates to a computer-readable medium on which the computer program is recorded.
• program instructions are taken from an external source and downloaded over a network.
• 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.
• 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 a interpreter, assembler, compiler, linker or locator.
• the program instructions are microcode, firmware instructions, state definition data, integrated circuit configuration data (e.g. VHDL), or object code.
• Program instructions are written in any combination of one or more programming languages, for example, object-oriented programming language (C++, JAVA, Python), procedural programming language (C language for example).
• the communication unit comprises at least one communication device allowing communication between the elements of the system and possibly between at least one element of the system and a device external to the system.
• 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 (wireless) communication link between elements of the system and/or between an element of the system and a device external to the system.
• the communications device may include any hardware, firmware and/or software suitable for communicating information between elements of the device to which the communications device belongs, for example via a data bus, or to an element external to the device.
• these devices include firmware and/or software hardware making it possible to establish, between them, a wired or wireless communication link. , for example Wi-Fi, Bluetooth, cellular or Ethernet.
• 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.
• the INT user interface includes, for example, an INTS interface and output and an INTE input interface.
• the input interface includes, for example, a keyboard or a pointing interface, such as a mouse, an optical pen, a touchpad, a remote control, a voice recognition device, a haptic device.
• a keyboard or a pointing interface such as a mouse, an optical pen, a touchpad, a remote control, a voice recognition device, a haptic device.
• the INTS output interface is designed to return information to a user, sensorially or electrically, for example visually or audibly.
• the output interface includes, for example, a display.
• the AFFD display step can be a step of restoring information by means other than a display.
• the INTS output interface can be the INTE input device, for example, in the case of a touchscreen tablet.

Landscapes

• Health & Medical Sciences (AREA)
• Physics & Mathematics (AREA)
• Engineering & Computer Science (AREA)
• Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
• General Health & Medical Sciences (AREA)
• Radiology & Medical Imaging (AREA)
• Life Sciences & Earth Sciences (AREA)
• Public Health (AREA)
• Medical Informatics (AREA)
• Biomedical Technology (AREA)
• High Energy & Nuclear Physics (AREA)
• Pathology (AREA)
• Primary Health Care (AREA)
• Signal Processing (AREA)
• Veterinary Medicine (AREA)
• General Physics & Mathematics (AREA)
• Animal Behavior & Ethology (AREA)
• Condensed Matter Physics & Semiconductors (AREA)
• Epidemiology (AREA)
• Surgery (AREA)
• Molecular Biology (AREA)
• Biophysics (AREA)
• Heart & Thoracic Surgery (AREA)
• Cardiology (AREA)
• Data Mining & Analysis (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Artificial Intelligence (AREA)
• Databases & Information Systems (AREA)
• Magnetic Resonance Imaging Apparatus (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=86656993

Family Applications (1)

Country Status (4)

Family Cites Families (1)

• 2023
  • 2023-01-25 FR FR2300699A patent/FR3145086A1/fr active Pending
• 2024
  • 2024-01-25 EP EP24702501.8A patent/EP4654884A1/de active Pending
  • 2024-01-25 CN CN202480021708.7A patent/CN121174981A/zh active Pending
  • 2024-01-25 WO PCT/EP2024/051797 patent/WO2024156813A1/fr not_active Ceased

Also Published As

Similar Documents

Legal Events