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WO2015073977A1 - Systèmes et procédés d'évaluation du risque de mort subite cardiaque - Google Patents

Systèmes et procédés d'évaluation du risque de mort subite cardiaque Download PDF

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Publication number
WO2015073977A1
WO2015073977A1 PCT/US2014/065974 US2014065974W WO2015073977A1 WO 2015073977 A1 WO2015073977 A1 WO 2015073977A1 US 2014065974 W US2014065974 W US 2014065974W WO 2015073977 A1 WO2015073977 A1 WO 2015073977A1
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WIPO (PCT)
Prior art keywords
shape
patient
risk
metric
heart
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PCT/US2014/065974
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English (en)
Inventor
Fijoy VADAKKUMPADAN
Katherine WU
Natalia A. Trayanova
Laurent YOUNES
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The Johns Hopkins University
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    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac
    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • the present invention relates to systems, methods and medical devices for use with determining risk for sudden cardiac death (SCD).
  • SCD sudden cardiac death
  • LVEF left ventricular ejection fraction
  • CMR cardiac magnetic resonance
  • 3D 3 -dimensional
  • the new field of computational anatomy offers rigorous mathematical and algorithmic tools for the detailed assessment of segmental differences in image-based cardiac geometry. See M. F. Beg, P. A. Helm, E. McVeigh, M. I. Miller, and R. L. Winslow: Computational Cardiac Anatomy Using MRI. Magnetic Resonance in Medicine 2004; 52: 1167 - 1174, the content of which is hereby incorporated herein by reference in its entirety.
  • an anatomical biomarker could also differentiate patients with a high risk of SCD from those who do not have SCD outcomes but instead eventually succumb to heart failure (HF), which is an important competing cause of death in patients with cardiomyopathy and for which the management approach can be quite different.
  • HF heart failure
  • a computer-implemented method for determining risk of sudden cardiac death in a patient includes: receiving imaging data of the patient's heart; constructing a three-dimensional geometrical representation of at least a portion of the patient's heart using the imaging data; calculating, using at least one data processor, a shape metric for each of a plurality of segmented myocardial wall regions of the patient's heart using the three-dimensional geometrical representation to provide a plurality of shape-metric values; and calculating, using the at least one data processor, a risk factor corresponding to a risk for sudden cardiac death in the patient based on the plurality of shape- metric values.
  • a medical imaging system for determining risk of sudden cardiac death in a patient includes a signal processing system, wherein the signal processing system includes a non-transitory computer-readable medium storing computer-executable instructions, the computer-readable medium holding one or more instructions for: receiving imaging data of the patient's heart; constructing a three- dimensional geometrical representation of at least a portion of the patient's heart using the imaging data; calculating a shape metric for each of a plurality of segmented myocardial wall regions of the patient's heart using the three-dimensional geometrical representation to provide a plurality of shape-metric values; and calculating a risk factor corresponding to a risk for sudden cardiac death in the patient based on the plurality of shape-metric values.
  • a non-transitory computer-readable storage medium for determining risk of sudden cardiac death in a patient stores computer- executable instructions that, when executed by at least one data processor, perform: receiving imaging data of the patient's heart; constructing a three-dimensional geometrical representation of at least a portion of the patient's heart using the imaging data; calculating, using at least one data processor, a shape metric for each of a plurality of segmented myocardial wall regions of the patient's heart using the three-dimensional geometrical representation to provide a plurality of shape-metric values; and calculating, using the at least one data processor, a risk factor corresponding to a risk for sudden cardiac death in the patient based on the plurality of shape-metric values.
  • FIG. 1 shows a processing pipeline of a computational framework, according to an embodiment of the current invention.
  • Fig. 2A-2C show reconstruction of 3D LV geometry from CMR image slices.
  • Fig. 2A shows an example slice with endocardial/epicardial contours, and landmarks corresponding to the septum, according to an embodiment of the current invention.
  • Fig. 2B shows 2D endocardial, epicardial, and septal masks for the slice of
  • FIG. 2A according to an embodiment of the current invention.
  • Fig. 2C shows a reconstructed 3D geometry in anterior view, according to an embodiment of the current invention.
  • Figs. 3A-3C shows shape metrics computed for the LV geometry shown in
  • Fig. 2C displayed on the endocardial surface in anterior view, according to an embodiment of the current invention.
  • Fig. 3A shows the shape metric of curvedness, according to an embodiment of the current invention.
  • Fig. 3B shows the shape metric of WT, according to an embodiment of the current invention.
  • Fig. 3C shows the shape metric of RWT, according to an embodiment of the current invention.
  • Figs. 4A-4G show segmentation of endocardial surfaces of patients.
  • Fig. 4A shows atlas geometry in anterior view, according to an embodiment of the current invention.
  • Fig. 4B shows superimposition of the patient geometry in Fig. 2C, and atlas geometry after affme transformation, according to an embodiment of the current invention.
  • Fig. 4C shows patient geometry, and atlas geometry after MC-LDDMM transformation, according to an embodiment of the current invention.
  • Fig. 4D shows posterior view of segmentation of the endocardial surface of the patient geometry into regions, according to an embodiment of the current invention.
  • Fig. 4E shows segmentation of an infarct region in an example 2D slice of the patient, according to an embodiment of the current invention.
  • Fig. 4F shows 3D reconstruction of an infarct region of the patient, along with the endocardial surface, according to an embodiment of the current invention.
  • Fig. 4G shows segmentation of the endocardial surface of the patient into transmurally infarcted, and the rest, according to an embodiment of the current invention.
  • Figs. 5A and 5B shows distribution of the TEI, according to an embodiment of the current invention.
  • Fig. 5A shows an anterior view of the distribution in the entire patient cohort, overlaid on the atlas endocardial surface, according to an embodiment of the current invention.
  • Fig. 5B shows mean transmural extent in the entire cohort and each of the patient groups, in each of the coronary artery territories, according to an embodiment of the current invention.
  • Fig. 6 shows mean values between group analysis of curvedness, WT, and
  • RWT between patient groups, and for coronary artery regions, according to an embodiment of the current invention.
  • Fig. 7 shows mean values of shape metrics in the non-transmurally infarcted or normal, and transmurally infarcted regions, within each patient group, according to an embodiment of the current invention.
  • the term "computer” is intended to have a broad meaning that may be used in computing devices such as, e.g., but not limited to, standalone or client or server devices.
  • the computer may be, e.g., (but not limited to) a personal computer (PC) system running an operating system such as, e.g., (but not limited to) MICROSOFT® WINDOWS®
  • PC personal computer
  • an operating system such as, e.g., (but not limited to) MICROSOFT® WINDOWS®
  • NT/98/2000/XP/Vista/Windows 7/8/etc. available from MICROSOFT® Corporation of Redmond, WA, U.S.A. or an Apple computer executing MAC® OS from Apple® of Cupertino, CA, U.S.A.
  • the invention is not limited to these platforms. Instead, the invention may be implemented on any appropriate computer system running any appropriate operating system. In one illustrative embodiment, the present invention may be implemented on a computer system operating as discussed herein.
  • the computer system may include, e.g., but is not limited to, a main memory, random access memory (RAM), and a secondary memory, etc.
  • Main memory random access memory (RAM), and a secondary memory, etc.
  • RAM may be a computer-readable medium that may be configured to store instructions configured to implement one or more embodiments and may comprise a random-access memory (RAM) that may include RAM devices, such as Dynamic RAM (DRAM) devices, flash memory devices, Static RAM (SRAM) devices, etc.
  • DRAM Dynamic RAM
  • SRAM Static RAM
  • the secondary memory may include, for example, (but is not limited to) a hard disk drive and/or a removable storage drive, representing a floppy diskette drive, a magnetic tape drive, an optical disk drive, a compact disk drive CD-ROM, flash memory, etc.
  • the removable storage drive may, e.g., but is not limited to, read from and/or write to a removable storage unit in a well-known manner.
  • the removable storage unit also called a program storage device or a computer program product, may represent, e.g., but is not limited to, a floppy disk, magnetic tape, optical disk, compact disk, etc. which may be read from and written to the removable storage drive.
  • the removable storage unit may include a computer usable storage medium having stored therein computer software and/or data.
  • the secondary memory may include other similar devices for allowing computer programs or other instructions to be loaded into the computer system.
  • Such devices may include, for example, a removable storage unit and an interface. Examples of such may include a program cartridge and cartridge interface (such as, e.g., but not limited to, those found in video game devices), a removable memory chip (such as, e.g., but not limited to, an erasable programmable read only memory (EPROM), or programmable read only memory (PROM) and associated socket, and other removable storage units and interfaces, which may allow software and data to be transferred from the removable storage unit to the computer system.
  • a program cartridge and cartridge interface such as, e.g., but not limited to, those found in video game devices
  • EPROM erasable programmable read only memory
  • PROM programmable read only memory
  • the computer may also include an input device may include any mechanism or combination of mechanisms that may permit information to be input into the computer system from, e.g., a user.
  • the input device may include logic configured to receive information for the computer system from, e.g. a user. Examples of the input device may include, e.g., but not limited to, a mouse, pen-based pointing device, or other pointing device such as a digitizer, a touch sensitive display device, and/or a keyboard or other data entry device (none of which are labeled).
  • Other input devices may include, e.g., but not limited to, a biometric input device, a video source, an audio source, a microphone, a web cam, a video camera, and/or other camera.
  • the input device may communicate with a processor either wired or wirelessly.
  • the computer may also include output devices which may include any mechanism or combination of mechanisms that may output information from a computer system.
  • An output device may include logic configured to output information from the computer system.
  • Embodiments of output device may include, e.g., but not limited to, display, and display interface, including displays, printers, speakers, cathode ray tubes (CRTs), plasma displays, light-emitting diode (LED) displays, liquid crystal displays (LCDs), printers, vacuum florescent displays (VFDs), surface-conduction electron-emitter displays (SEDs), field emission displays (FEDs), etc.
  • the computer may include input/output (I/O) devices such as, e.g., (but not limited to) communications interface, cable and
  • communications path may include, e.g., but are not limited to, a network interface card, and/or modems.
  • the output device may communicate with processor either wired or wirelessly.
  • a communications interface may allow software and data to be transferred between the computer system and external devices.
  • data processor is intended to have a broad meaning that includes one or more processors, such as, e.g., but not limited to, that are connected to a
  • the term data processor may include any type of processor, microprocessor and/or processing logic that may interpret and execute instructions (e.g., for example, a field programmable gate array (FPGA)).
  • the data processor may comprise a single device (e.g., for example, a single core) and/or a group of devices (e.g., multi-core).
  • the data processor may include logic configured to execute computer-executable instructions configured to implement one or more embodiments.
  • the instructions may reside in main memory or secondary memory.
  • the data processor may also include multiple independent cores, such as a dual-core processor or a multi-core processor.
  • the data processors may also include one or more graphics processing units (GPU) which may be in the form of a dedicated graphics card, an integrated graphics solution, and/or a hybrid graphics solution.
  • graphics processing units GPU
  • GPU graphics processing units
  • data storage device is intended to have a broad meaning that includes removable storage drive, a hard disk installed in hard disk drive, flash memories, removable discs, non-removable discs, etc.
  • various electromagnetic radiation such as wireless communication, electrical communication carried over an electrically conductive wire (e.g., but not limited to twisted pair, CAT5, etc.) or an optical medium (e.g., but not limited to, optical fiber) and the like may be encoded to carry computer-executable instructions and/or computer data that embodiments of the invention on e.g., a communication network.
  • These computer program products may provide software to the computer system.
  • a computer-readable medium that comprises computer-executable instructions for execution in a processor may be configured to store various embodiments of the present invention.
  • Some embodiments of the current invention provide novel image-based, patient-specific metrics of myocardial wall shape to predict SCD risk.
  • the metrics are three- dimensional (3D), localized to regions of the heart, and can be easily measured using widespread clinical imaging modalities such as, but not limited to, magnetic resonance imaging (MRI), and computed tomography (CT). These metrics can be statistically combined to generate an index, which can help significantly improve SCD risk stratification.
  • the imaging data can be at least one of magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), ultrasound, or nuclear tracer three-dimensional imaging data.
  • Fig. 1 shows how a patient heart image is processed with our pipeline, including reconstruction of 3D LV geometry, segmentation of endocardial surface, computation of 3D shape metrics, and regionwise statistical analysis.
  • the data acquisition and the components of the pipeline are described in the following sections.
  • Fig. 1 is a schematic representation of a medical imaging system, a non- transitory computer readable medium and a computer-implemented method for determining risk of sudden cardiac death in a patient.
  • the method can include receiving imaging data of the patient's heart. Data acquisition can include using LGE-CMR images.
  • the method can include constructing a three-dimensional geometrical representation of at least a portion of the patient's heart using the imaging data; calculating or computing a shape metric for each of a plurality of segmented myocardial wall regions of the patient's heart using the three-dimensional geometrical representation to provide a plurality of shape-metric values; and calculating a risk factor corresponding to a risk for sudden cardiac death in the patient based on the plurality of shape-metric values.
  • one embodiment of the invention can include region-wise statistical analysis.
  • Fig. 2 shows that in one embodiment, the constructing the three-dimensional geometrical representation of at least a portion of the patient's heart using the imaging data can include reconstruction of 3D LV geometry.
  • LV endocardium and epicardium can be semi-automatically contoured. Septal part of the endocardial contour can then be manually identified by placing two landmark points near the right-ventricular (RV) insertion points (Fig. 2A). From the contours and landmark points, three sets of 2D binary masks, each set implicitly representing the LV endocardium, LV epicardium, and septal endocardium can be constructed (Fig. 2B).
  • Each set of 2D masks can then be interpolated to build a 3D binary mask at 1mm isotropic resolution, based on the so- called variational implicit functions strategy.
  • the geometry image of the LV wall can be generated by combining the three 3D masks (Fig. 2C).
  • the semiautomatic contouring of the LV endocardium and epicardium can be performed using CineTool® (General Electric Healthcare).
  • the landmarks corresponding to the RV insertion points can be placed using a graphical user interface developed in-house in MATLAB® (Mathworks, Inc.).
  • the 2D binary masks that implicitly represented LV endocardium and LV epicardium can be constructed by labeling the pixels that lie within the contours.
  • the coefficients of the linear combination can be calculated by solving the linear system that results from enforcing the boundary constraints. More details on this interpolation method are available elsewhere.
  • 3D masks for LV endocardium, LV epicardium, and septal endocardium can be generated.
  • the geometry image of the LV wall can be generated by combining the 3D masks.
  • the final geometry image has different intensities for the LV chamber, LV free-wall, and septum.
  • Fig. 2 shows reconstruction of 3D LV geometry from CMR image slices.
  • A Example slice with endocardial/epicardial contours, and landmarks corresponding to the septum.
  • B The 2D endocardial, epicardial, and septal masks for the slice.
  • C The reconstructed 3D geometry in anterior view.
  • Figure 3 shows calculating or computing a shape metric at each point on the endocardial surface of the patient's heart using the three-dimensional representation. These point-wise shape metric values can be averaged to compute a shape metric for each of a plurality of segmented myocardial wall regions of the patient's heart to provide a plurality of shape-metric values.
  • the shape metric can be one of curvedness, wall thickness (WT), and relative wall thickness (RWT).
  • An embodiment of the current invention can include calculating a second shape metric for a second plurality of segmented myocardial wall regions of the patient's heart using the three-dimensional geometrical representation to provide a second plurality of shape-metric values, wherein the calculating the risk for sudden cardiac death in the patient is based on the predetermined semi-empirical model utilizing as input the first-mention plurality of shape-metric values and the second plurality of shape-metric values.
  • the second shape metric can be one of curvedness, wall thickness (WT), and relative wall thickness (RWT).
  • An embodiment of the current invention can include calculating a third shape metric for a third plurality of segmented myocardial wall regions of the patient's heart using the three-dimensional geometrical representation to provide a third plurality of shape-metric values, wherein the calculating the risk for sudden cardiac death in the patient is based on the predetermined semi-empirical model utilizing as input the first-mention plurality of shape- metric values, the second plurality of shape metric values and the third plurality of shape- metric values.
  • the third shape metric can be one of curvedness, wall thiclcness (WT), and relative wall thickness (RWT).
  • the first-mention shape metric, the second shape metric, and the third shape metric can be at least one of curvedness, wall thiclcness (WT), and relative wall thiclcness (RWT).
  • WT wall thiclcness
  • RWT relative wall thiclcness
  • the first-mentioned plurality of segmented myocardial wall regions, the second plurality of segmented myocardial wall regions and the third plurality of segmented myocardial wall regions are the same plurality of wall regions.
  • Embodiments of the invention can include a framework that uses CMR, advanced image processing, and computational anatomy tools to compare 3D LV endocardial surface curvature, wall thickness (WT), and relative wall thickness (RWT) between patient groups.
  • a methodology can be retrospectively applied to data from patients with ischemic cardiomyopathy selected for ICD implantation on the basis of reduced LVEF, followed by implantation for clinical events, and divided into groups with differing SCD risk on the basis of follow-up time.
  • 3D LV shape metrics can identify patients at the highest risk for SCD. Shape metrics alone may differentiate between SCD and HF outcomes. Results demonstrate, for the first time, that there exist significant local 3D LV shape dissimilarities between the patient groups with differing SCD risks.
  • Fig. 3 shows shape metrics computed for the LV geometry shown in Fig. 2C, displayed on the endocardial surface in anterior view.
  • A Curvedness.
  • B WT.
  • C RWT.
  • curvedness of the surface can be computed. Curvedness can characterize the deviation of a surface from flatness, and can be defined as the root mean square of principal curvatures. See L. Zhong, Y. Su, S.-Y. Yeo, R.-S. Tan, D. N. Ghista, and G. Kassab: Left ventricular regional wall curvedness and wall stress in patients with ischemic dilated cardiomyopathy.
  • curvedness can be measured from an interior surface of the heart. In another embodiment, curvedness can be measured from an exterior surface of the heart.
  • curvedness can be measured from both the interior and the exterior surfaces of the heart.
  • the WT at an endocardial surface voxel can be calculated as the distance to the nearest voxel that lied along the epicardium.
  • the RWT at an endocardial surface voxel can be computed as the product of curvedness and WT at that voxel (Fig. 3).
  • This definition of RWT is a 3D extension of a 2D echocardiographic concept, where RWT is defined as the ratio of posterior or septal wall thickness to internal radius of the LV chamber in diastole. See M. A. Konstam, D. G. Kramer, A. R. Patel, M. S. Maron, and J. E.
  • Vg and H(g) can denote the gradient and Hessian of g, and / can identity matrix.
  • g we evaluated two options for g, namely the 3D thin plate spline function , and a Gaussian smoothed version of the 3D mask, where both / and the mask can be built from the set of 2D binary masks corresponding to the endocardium as described in section 2 of this online supplement. In one embodiment, we adopted the latter option because it can be less susceptible to noise.
  • Curvedness of the endocardial surface can be defined as the root mean square
  • Fig. 4 shows segmentation of patient endocardial surface.
  • the endocardial surface of each patient LV geometry can be segmented based on predetermined regions of the heart.
  • the predetermined regions of the heart can be coronary artery regions. See M. D. Cerqueira, N. J. Weissman, V. Dilsizian, et al.: 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. Circulation 2002; 105: 539 - 542, the content of which is hereby incorporated herein by reference in its entirety.
  • one LV geometry (referred to as the atlas) can be selected, and semi-automatically segmented into the regions using a variant of the method described in Su, et al. See Y. Su, L. Zhong, C.-W. Lima, D. Ghistac, T. Chua, and R.-S. Tan: A geometrical approach for evaluating left ventricular remodeling in myocardial infarct patients, Computer Methods and Programs in Biomedicine 2012; 108: 500 - 510, the content of which is hereby incorporated herein by reference in its entirety.
  • This atlas can be then deformed to match the patient LV geometry using affine transformation and the computational anatomy algorithm termed multi-channel large deformation diffeomorphic metric mapping (MC-LDDMM), with the 3D endocardial, epicardial, and septal LV masks described above as channels.
  • MC-LDDMM multi-channel large deformation diffeomorphic metric mapping
  • These deformations provided, for each point in the atlas, the anatomically corresponding points on patient LV geometries.
  • each voxel on the endocardial surface of a patient LV geometry can be classified as belonging to the same region as the anatomically corresponding point on the atlas (Fig. 4A-D).
  • an atlas is a representative anatomy which can be deformed to match different patient anatomies.
  • Computational anatomy atlases are typically labeled with pertinent data such as various anatomical regions, just as a world atlas is overlaid with geographical features.
  • the atlas can be labelled with regions using a variant of the segmentation methodology described in Su, et al.
  • the 3D atlas geometry can be divided into apex, apical level, mid-level, and basal level with 3 planes, all parallel to the short axis imaging plane.
  • the first of the 3 planes passed through the most apical point of the LV chamber can be located l/3rd the way from the first plane to the most basal short axis image plane, and the third 2/3rd the way from the first plane to the most basal short axis image plane.
  • the atlas LV wall in each short axis basal slice can be then divided into 6 regions. To perform this division, a reference direction that pointed from the centroid of the LV chamber to the midpoint of the endocardial contour that belonged to the septum was computed. Then, for each voxel in the LV wall, the clockwise angle between the reference direction, and the vector that connected the centroid and the voxel can be computed. The voxel can be labelled as belonging to American Heart Association (AHA) regions 1-6 based on this angle. Similarly, the LV wall in mid and apical levels can be labelled with AHA regions 7-12, and 13-16, respectively.
  • AHA American Heart Association
  • Segment 17 can be excluded in the final analysis because of limited resolution at the LV apex.
  • the deformation of the atlas to match a patient geometry can be achieved using a combination of affine transformation and multi-channel large deformation diffeomorphic metric mapping (MCLDDMM).
  • MLDDMM multi-channel large deformation diffeomorphic metric mapping
  • the elements of A and b can be computed based on a set of corresponding landmark points identified in the atlas and patient geometries.
  • the affine transformation provided an initial registration for MC-LDDMM, which deformed the affine-transformed atlas geometry further to match the patient geometry, using a diffeomorphic (invertible and smooth) transformation.
  • MC-LDDMM computes a flow of diffeomorphisms t ' to transform la to match Ip, where ⁇ c
  • R3 is the 3D cube in which the image data are defined, te[0, 1], and v is a smooth, compactly supported, time-dependent velocity vector field such that
  • the initial diffeomorphism 0o is the identity transformation.
  • the final diffeomorphism ⁇ p can be calculated by integrating the optimal vector field given by
  • c is the number of channels, and / 3 ⁇ 4 denotes image corresponding to the i th channel in the atlas.
  • v is defined as
  • v ⁇ Lf ⁇
  • L 2 to enforce smoothness on the vector fields veV, where L ( ⁇ aV 2 + y) 2 /3x3 is a differential operator of the Cauchy- Navier type.
  • the parameters a and ⁇ control the elasticity of the transformation, and
  • is the L 2 norm of square integrable functions defined on ⁇ .
  • the optimal solution can be computed by means of a gradient descent search.
  • the diffeomorphic properly of MC- LDDMM can guarantee that the atlas does not "fold over" itself during deformation, thereby preserving the integrity of anatomical structures.
  • the infarct zone was planimetered on each short-axis 2D slice using a method employed previously.
  • the infarct zone in each 2D slice of the patient LGE-CMR image can be planimetered, and the result used to reconstruct a 3D geometry of the infarct.
  • a trained observer identified a region of interest (ROI) in the remote, non- infarcted myocardium, and computed the maximum image intensity in this ROI as the peak remote intensity.
  • ROI region of interest
  • the observer then can loosely outline the hyper enhanced region, and all voxels in this region with intensity above the peak remote intensity can be labeled as belonging to the infarct zone.
  • planimetry please refer to Wu KC, Gerstenblith G, Guallar E, Marine JE, Dalai D, Cheng A, Marban E, Lima JAC, Tomaselli GF, Weiss RG: Combined Cardiac MRI and C-Reactive Protein Levels Identify a Cohort at Low Risk for Defibrillator Firings and Death. Circulation: Cardiovascular Imaging 2012; 5: 178 - 186.
  • each 2D slice after planimetry can be converted into a grayscale image, where each pixel can be assigned a value whose magnitude can be the distance to the nearest point on the infarct zone boundary. The sign of this assigned value can be negative if the pixel was within the infarct zone, and positive if outside.
  • the 2D slices can be then interpolated linearly to obtain a 3D image, which can be then thresholded for values below zero, to obtain a 3D binary image of the infarct geometry. More details on this reconstruction technique can be found elsewhere.
  • a 3D reconstruction of the infarct geometry can be obtained at 1mm isotropic resolution, via a shape-based binary interpolation method. See S. P. Raya and J. K. Udupa: Shape-based interpolation of multidimensional objects. IEEE Transactions on Medical Imaging 1990; 9: 32 - 42, the content of which is hereby incorporated herein by reference in its entirety.
  • a line segment can be computed by connecting v to the nearest epicardial surface voxel, and the transmural extent of the infarct (TEI) at v can be calculated as the proportion of this line segment that intersects with the 3D reconstruction of the infarct geometry.
  • each endocardial surface voxel with TEI > 80% can be classified as transmurally infarcted (Fig. 4E-G). This particular threshold is often used to delineate transmural scar. See Boye P, Abdel-Aty H, Zacharzowsky U, Bohl S, Schwenke C, van der Geest RJ, Dietz R, Schirdewan A, Schulz-Menger J.
  • Figure 4 shows segmentation of endocardial surfaces of patients.
  • A Atlas geometry in anterior view.
  • B Superimposition of the patient geometry in Fig. 2C, and atlas geometry after affine transformation.
  • C The patient geometry, and the atlas geometry after MC-LDDMM transformation.
  • D Posterior view of segmentation of the endocardial surface of the patient geometry into the 17 AHA regions.
  • E Segmentation of infarct region in an example 2D slice of the patient.
  • F 3D reconstruction of the infarct region of the patient, along with the endocardial surface.
  • G Segmentation of the endocardial surface of the patient into transmurally infarcted, and the rest.
  • the calculating a risk factor step can include a predetermined semi-empirical model that utilizes as input the plurality of shape metrics.
  • Statistical analyses can include baseline characteristics summarized as means or proportions for each patient group, and statistically compared between groups.
  • Figure 5 shows distribution of the TEL
  • A Anterior view of the distribution in the entire patient cohort, overlaid on the atlas endocardial surface.
  • B Mean transmural extent in the entire cohort and each of the patient groups, in each of the coronary artery territories. Brackets with different symbols can indicate significant differences between patient groups for the same coronary artery region, and between coronary artery regions for the same group.
  • Figure 5A and 5B show distribution of the TEI, according to an embodiment of the current invention.
  • Fig. 5A shows an anterior view of the distribution in the entire patient cohort, overlaid on the atlas endocardial surface, according to an embodiment of the current invention.
  • Fig. 5B shows mean transmural extent in the entire cohort and each of the patient groups, in each of the coronary artery territories, according to an embodiment of the current invention.
  • the 3d distribution of TEI can be derived by calculating, at each point p on the atlas endocardial surface, the mean and SD of TEIs at each point on patient LVs that corresponded to p according to the deformations of the atlas geometry.
  • the mean TEI in each of the three coronary arterial territories namely left anterior descending artery (LAD), right coronary artery (RCA), and left circumflex artery (LCX)
  • LAD left anterior descending artery
  • RCA right coronary artery
  • LCX left circumflex artery
  • the myocardial wall regions can include territories of at least one of: left anterior descending artery, left circumflex artery, and right coronary artery.
  • the myocardial wall regions can include the territories of left anterior descending artery, left circumflex artery, and right coronary artery.
  • the statistical comparisons can be performed in the 3 coronary artery regions. Analysis by coronary artery regions is physiologically very meaningful in the context of myocardial infarction, as the segments are inter-related and dependent. This approach also significantly (by a factor of over 5) reduces the number of simultaneous statistical comparisons, and thereby decreases the probability of multiple comparison (Type I) errors.
  • each patient i can be computed as
  • n r ,i is the number of voxels in the region
  • v , r ,i is the value of the shape metric at voxel v in the region.
  • the mean shape metrics can be corrected for confounding effects of covariates, by fitting a linear regression model that predicts the former by the latter, and taking the residual. It is important to perform such a correction, as demonstrated elsewhere in the context of shape analysis of brain structures.
  • s r ,i were the uncorrected mean curvedness, WT, or RWT, and c the value of covariate c.
  • the coefficients oc can be determined by linear regression.
  • Miller M Younes L, Ratnanather J, Brown T, Trinh H, Postell E, Lee D, Wang M, Mori S, O'Brien R, Albert M, Team. BR: The diffeomorphometry of temporal lobe structures in preclinical Alzheimer's disease. Ncuroimage: Clinical 2013 ; 3 : 352 - 360; and Younes L, Ratnanather J, Brown T, et al. : Regionally selective atrophy of subcortical structures in prodromal HD as revealed by statistical shape analysis. Human Brain Mapping 2012; 35 : 792 - 809, the contents of which are hereby incorporated herein by reference in their entirety.
  • the Wilcoxon rank sum test statistic can be computed in each coronary artery region for a large number of random assignments of the group labels to the subjects (i.e., permutations).
  • the maximum test statistic tWx in each permutation ⁇ can then be calculated, and compared with the test statistic t c t me obtained with true group labels for each coronary artery region c.
  • the p-value for coronary artery region c can be then calculated as the fraction of times t x exceeded t c tme.
  • a large number of permutations of the sample can be generated by swapping the mean shape metric values of the non-transmurally infarcted or normal coronary artery regions with those of transmurally infarcted ones in a randomly selected subset of the group.
  • the maximum test statistic in each permutation can be then computed, and compared with the test statistic corresponding to the true shape metrics, to generate a p-value for each coronary artery region.
  • permutation tests such as the ones we employed make minimal assumptions about the data, are effective in eliminating multiple comparison errors in shape analysis, and are widely used in the statistics community. See G. W. Cordor and D. I. Foreman, Nonparametric Statistics for Non-Statisticians. New Jersey: Wiley, 2009, the content of which is hereby incorporated herein in their entirety.
  • the predetermined semi-empirical model can correspond to a high-risk model and the risk for sudden cardiac death in the patient can correspond to a high risk when the risk factor meets a pre-determined semi-empirical high-risk threshold.
  • the predetermined semi-empirical model can correspond to a low-risk model and wherein the risk for sudden cardiac death in the patient can correspond to a low risk when the low-risk model meets a pre-determined semi-empirical low-risk threshold.
  • the predetermined semi-empirical model can include at least one of logistic regression, linear regression and a proportional hazard model.
  • the predetermined semi-empirical model is a logistic regression model taking as input shape-metric values and outputting probabilities for the risk factor.
  • the predetermined semi-empirical model can be a Cox proportional hazard model.
  • the predetermined semi-empirical model can be a linear regression model.
  • Fig. 6 shows brackets with different symbols indicating significant differences. There were no significant differences between groups 1 and 2.
  • Fig. 7 shows brackets with different symbols indicate significant differences.
  • LVEF low left-ventricular ejection fraction
  • SCD sudden cardiac death
  • Three-dimensional imaging data often refers to images acquired in isotropic resolutions (same resolution in x, y, and z directions). Some embodiments do not necessarily require isotropic images from the scanner. For example, the images that we use in the examples below have very low resolution in the z-direction, so it's similar to a collection of 2D slices. The methodology in those examples uses an interpolation technique to reconstruct the 3D geometry of the myocardial wall from the collection of 2D slices.
  • the shape metrics that we use incorporate geometrical parameters that contribute to wall stress, which in turn is correlated with arrhythmia and heart failure. These 3D shape metrics can be utilized to stratify SCD risk of the patient, and help guide prophylactic treatment decisions, including deployment of implantable cardioverter defibrillators (ICDs).
  • ICDs implantable cardioverter defibrillators
  • LVEF left-ventricular ejection fraction
  • SCD sudden cardiac death
  • the data acquisition includes LGE-CMR images of 61 patients with ischemic cardiomyopathy and LVEF ⁇ 35%, which represented a random sample from the CMR arm of the prospective observational study of implantable cardioverter defibrillators (CMR-PROSE-ICD) at Johns Hopkins University.
  • CMR-PROSE-ICD implantable cardioverter defibrillators
  • 61 patients were imaged, implanted with ICDs for primary prevention of SCD, and then followed for events, including appropriate ICD firings, sudden arrhythmic death (SAD), and death or hospitalization due to heart failure (HF).
  • SAD sudden arrhythmic death
  • HF heart failure
  • group 0 consisted of 28 patients with no events during follow up
  • group 1 included 18 patients who either suffered from SAD or whose ICDs fired appropriately
  • group 2 comprised of 15 patients who died of or were hospitalized for HF, but did not have an arrhythmic event.
  • group 1 included 18 patients who either suffered from SAD or whose ICDs fired appropriately
  • group 2 comprised of 15 patients who died of or were hospitalized for HF, but did not have an arrhythmic event.
  • the 61 patients were selected such that the number of patients with and without events roughly matched. Patients who had both an arrhythmic event and HF were included in group 1.
  • Fig. 1 in this supplement displays the 2D short- axis slices of the LGE-CMR image acquired for an example patient. The figure illustrates that, due to the limited resolution of the image in slice thickness, the apex of the heart is not visible in the image. Therefore, AHA region 17 was excluded from all statistical analyses in this study.
  • Methods Utilizing clinical cardiac magnetic resonance (CMR) imaging and computational anatomy tools, a novel computational framework to compare three- dimensional (3D) LV endocardial surface curvedness, wall thickness (WT), and relative wall thickness (RWT) between patient groups was implemented.
  • the framework was applied to CMR data of 61 patients with ischemic cardiomyopathy who were selected for prophylactic implantable cardioverter defibrillator treatment based on reduced LVEF.
  • the patients were classified by outcome: group 0 had no events; group 1, arrhythmic events, and group 2, heart failure (HF). Segmental differences in LV shape were assessed.
  • the current examples use three types of shape metrics, the general concepts of the current invention are not limited to a particular number of shape metrics. Some embodiments can use one, two, three or even more than three shape metrics.
  • patients in groups 1 and 2 Compared to patients with no events, patients in groups 1 and 2 generally had lower regional curvedness, WT, and RWT reflecting wall thinning and stretching/flattening.
  • Fig. 5 shows the anterior view of the spatial distribution of TEI in the patient cohort, as well as comparisons of the mean TEI between groups, and between coronary artery territories. The mean TEI in the LAD region was significantly higher in 4 comparisons. There was only one significant inter-group
  • Figure 5 also displays the spatial distributions of the shape metrics in the entire patient cohort.
  • Figure 6 shows a comparison of the shape metrics between groups in each of the coronary artery regions. In all regions, groups with an event had lower mean curvedness, WT, and RWT. Of the 27 intergroup comparisons, 9 were statistically significant. Among the 3 shape metrics, the maximum number of significant differences was found in RWT. There were no significant differences in the shape metrics between groups 1 and 2.
  • LV anatomical parameters alone may not be able to definitively differentiate between the 2 competing causes of death in patients with cardiomyopathy, that is, SAD and pump failure. Nonetheless, incorporating these shape metrics into SCD risk prediction has the potential to help enhance the accuracy of image-based risk stratification approaches by accounting for individual differences in regional LV anatomy that are not adequately described by the currently used global LV metrics and by identifying a cohort with favorable LV anatomy that portends good prognosis.
  • Ackerman, et al. Geometric assessment of regional left ventricular remodeling by three- dimensional echocardiographic shape analysis correlates with left ventricular function.
  • Thygesen Ventricular arrhythmias in the acute and chronic phases after acute myocardial infarction. Effect of intervention with captopril. Circulation 1994; 90: 101— 107, the contents of which are hereby incorporated herein in their entirety.
  • LV remodeling is correlated with infarct size, and so are arrhythmic events. See P. Gaudron, C. Eilles, I. Kugler, and G. Ertl: Progressive left ventricular dysfunction and remodeling after myocardial infarction. Potential mechanisms and early predictors. Circulation 1993; 87: 755 - 763; J. N. Cohn, R. Ferrari and N. Sharpe: Cardiac remodeling— concepts and clinical implications: a consensus paper from an international forum on cardiac remodeling.
  • a novel computational framework and findings in this study constitute an important step toward utilization of LV shape indices in clinical risk stratification of cardiomyopathy patients.
  • shape indices according to some embodiments of the current invention could potentially be used in combination with existing predictors of SCD such as global LV volume and scar characteristics, to train an empirical model, which, given the shape metrics of a patient, may non-invasively and reliably compute a personalized risk score that reflects the patient's susceptibility to SCD.
  • the framework that we have developed is equally applicable to patients with non-ischemic etiologies or with LVEF > 35%, and can incorporate any point-wise shape metric.
  • our methodology can be straightforwardly extended to use other clinical imaging modalities such as cine MRI sequences or cardiac computed tomography.
  • EF ejection fraction
  • ICD implantable cardioverter defibrillator
  • LGE-CMR gadolinium enhanced cardiac magnetic resonance imaging
  • LV left ventricular
  • SAD sudden arrhythmic death
  • HF heart failure

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Abstract

La présente invention concerne un procédé informatique, un système d'imagerie médicale et un support d'enregistrement permanent lisible par ordinateur permettant de déterminer le risque de mort subite cardiaque chez un patient, ledit procédé comprenant les étapes consistant à recevoir des données d'imagerie relatives au cœur dudit patient; à construire une représentation géométrique en trois dimensions d'au moins une partie du cœur dudit patient au moyen desdites données d'imagerie; à calculer, au moyen d'au moins une unité de traitement de données, un indicateur de forme pour chaque zone d'une pluralité de zones de la paroi myocardique segmentée du cœur du patient au moyen de ladite représentation géométrique en trois dimensions afin d'obtenir plusieurs valeurs d'indication de forme; et à calculer, au moyen de ladite ou desdites unités de traitement de données, un facteur de risque correspondant à un risque de mort subite cardiaque sur la base de ladite pluralité de valeurs d'indication de forme.
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