Quantification method and system for myocardial deformation of left ventricle

Abstract

The invention discloses a left ventricular myocardium deformation quantification method and system. The method comprises the following steps: obtaining a dynamic three-dimensional curved surface sequence of left ventricular myocardium in a complete heartbeat cycle; obtaining a Beltrami coefficient corresponding to each fixed anatomical point in each time frame according to a mapping relation between each frame in the three-dimensional curved surface sequence and a unified parameter domain, and obtaining a local deformation scalar of each frame in the three-dimensional curved surface sequence based on the coefficient; and constructing an intrinsic deformation trajectory curve of each fixed anatomical point on the unified parameter domain in the cardiac cycle, and determining a deformation feature vector according to the curve. According to the method, homeomorphic mapping from a myocardial curved surface at each time point to a unified parameter domain is established, three-dimensional motion is coded into sequential evolution of a Beltrami coefficient on a two-dimensional parameter point, the certainty of mapping is utilized to replace the estimability of frame-by-frame registration in a traditional three-dimensional space, drift and accumulative errors of corresponding points are fundamentally and effectively avoided, and the accuracy of the method is improved. And accurate quantification with high time-space consistency on myocardial deformation can be realized.

Description

Technical Field

[0001] This disclosure relates to the field of medical image processing technology, and in particular to a method and system for deforming left ventricular myocardium. Background Technology

[0002] Accurate assessment of left ventricular motion patterns is crucial for evaluating cardiac function and screening for early diseases. Traditional static analysis methods, based on specific cardiac phases (such as end-diastole or end-systole), can provide scalar parameters such as ejection fraction, but due to oversimplification, they fail to capture the spatiotemporal continuity of cardiac motion.

[0003] Early research relied primarily on manual or semi-automatic boundary recognition of two-dimensional images. This approach failed to accurately characterize complex three-dimensional nonlinear deformations and individual differences. Furthermore, the non-rigid deformation characteristics of the heart made it difficult for traditional registration methods to maintain stable correspondences across time frames, leading to accumulated errors. In recent years, deep learning technology has significantly improved the automation level of cardiac motion tracking. The fusion architecture of convolutional networks and Transformers has shown great potential in three-dimensional dynamic cardiac modeling, effectively processing massive amounts of data and learning complex motion patterns. At the same time, the rapid development of three-dimensional reconstruction technology has opened up new avenues for cardiac motion analysis. Methods based on point cloud registration and graph neural networks have achieved significant breakthroughs in establishing spatiotemporal correspondences. By integrating 4D spatiotemporal information, richer biomarkers can be provided for clinical diagnosis.

[0004] However, these advanced technologies still have significant limitations: while deep learning-based methods have significantly improved processing speed, they generally face inherent problems such as inconsistent motion fields, poor model interpretability, and high computational complexity; while 3D point cloud methods face severe challenges in maintaining mesh structure consistency and ensuring the continuity of physiological motion, limiting their widespread application in clinical practice. Summary of the Invention

[0005] In view of this, the present disclosure provides a method and system for left ventricular myocardial deformation, which can solve the problems of incomplete, unstable and low-precision left ventricular information obtained by the prior art.

[0006] This disclosure provides a method for left ventricular myocardial deformation, including: Obtain a three-dimensional surface sequence of the dynamic left ventricular myocardium within a complete cardiac cycle; Obtain the mapping relationship between each frame in the three-dimensional surface sequence and the unified parameter domain, and obtain the Beltrami coefficient corresponding to each fixed anatomical point in the unified parameter domain at each time frame based on the mapping relationship; Based on the Beltrami coefficients, the local deformation scalar of each frame in the three-dimensional surface sequence is obtained; Based on the local deformation scalar of each frame, construct the intrinsic deformation trajectory curve of each fixed anatomical point in the unified parameter domain during the cardiac cycle; The deformation feature vector is determined based on the intrinsic deformation trajectory curve.

[0007] The left ventricular myocardial deformation quantification method disclosed in this application takes a sequence of left ventricular myocardial surfaces with stable point correspondences on a unified parameter domain as the processing object. First, based on the mapping relationship from the surface to the parameter domain, the Beltrami coefficient corresponding to each point on the surface is calculated, and its maximum stretch quotient is derived as a local deformation scalar. Then, within a complete cardiac cycle, the temporal changes of the deformation scalar are tracked point by point to construct its trajectory curve. Finally, a set of characteristic parameters representing the core physical properties of myocardial motion are extracted and quantified from the trajectory. This application constructs a fixed parameter domain as a unified geometric reference framework. By establishing a homeomorphic mapping from the myocardial surface at each time point to this common parameter domain, three-dimensional motion is encoded as the temporal evolution of Beltrami coefficients at two-dimensional parameter points. The determinism of the mapping replaces the estimation of frame-by-frame registration in traditional three-dimensional space, fundamentally and effectively avoiding corresponding point drift and accumulated errors, and enabling accurate quantification of myocardial deformation with high spatiotemporal consistency.

[0008] The above description is merely an overview of the technical solution disclosed herein. In order to better understand the technical means of this disclosure and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating the method for left ventricular myocardial deformation according to an embodiment of this disclosure.

[0011] Figure 2 A schematic diagram of the spatiotemporal distribution of the Beltrami coefficient field on the surface of the heart, provided for embodiments of this disclosure. Detailed Implementation

[0012] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0013] It should be understood that the following specific examples illustrate the implementation of this disclosure, and those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0014] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0015] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0016] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0017] Reference Figure 1 This application discloses a method for deforming left ventricular myocardium, comprising: S100 obtains a three-dimensional surface sequence of the dynamic left ventricular myocardium within a complete cardiac cycle.

[0018] The three-dimensional surface sequence contains images of multiple time points throughout a complete heartbeat cycle (i.e., from end-diastole to end-systole and back to end-diastole).

[0019] In this embodiment, specifically, left ventricular myocardial segmentation is performed on each frame of a complete cardiac magnetic resonance short-axis image sequence for one cardiac cycle to obtain the myocardial boundary contour. Then, a surface reconstruction algorithm is used to reconstruct the myocardial boundary contour of all frames, generating a three-dimensional surface sequence of the left ventricular myocardium. Further, the left ventricular myocardial segmentation is performed frame-by-frame. For each static image in this dynamic sequence, pixels belonging to the left ventricular myocardium are distinguished from and marked with other parts (such as blood, other cardiac structures, and background). Specific segmentation methods may include: manual delineation (drawn manually by an experienced physician), semi-automatic interaction (the algorithm provides an initial contour, which is then manually corrected and confirmed), and fully automatic segmentation algorithms (completely performed by computer algorithms, such as a deep learning network based on U-Net).

[0020] The myocardial boundary contour line includes the endocardial boundary contour line and the epicardial boundary contour line. The endocardial boundary contour line is the inner edge of the junction between the left ventricular cavity and the myocardium, and the epicardial boundary contour line is the outer edge of the junction between the myocardium and the pericardium or surrounding tissues. These two lines together define the boundary and thickness of the myocardial wall.

[0021] In this embodiment, the obtained 3D surface sequence is raw data, without parameterization, and each frame is independent. The 3D surface sequence { Specifically, it is {S1,..., St..., ST}, where St represents the left ventricular surface at time point t.

[0022] S200: Obtain the mapping relationship between each frame in the three-dimensional surface sequence and the unified parameter domain, and obtain the Beltrami coefficient corresponding to each fixed anatomical point in the unified parameter domain at each time frame based on the mapping relationship.

[0023] In this context, different frames representing the same anatomical location in the three-dimensional surface sequence have the same fixed anatomical point coordinates on the unified parameter domain. That is, each frame in the three-dimensional surface sequence has a spatiotemporal vertex correspondence with the unified parameter domain, and each frame is independently mapped to the parameter domain. The unified parameter domain is preferably a two-dimensional parameter domain.

[0024] Beltrami coefficient (i.e., maximum stretch quotient) is : The coefficient is a complex-valued function. For about The conjugate derivative of , ; For about The derivative of .

[0025] Among them, the surface for each time frame Each independently calculated a common two-dimensional parameter domain from the three-dimensional surface. mapping The mapping is preferably a quasi-conformal mapping to control the geometric distortion it introduces. The mapped 3D surface sequence has established spatiotemporal vertex correspondences through prior 3D motion tracking or registration techniques.

[0026] S300, based on Beltrami coefficients, obtains the local deformation scalar for each frame in a three-dimensional surface sequence.

[0027] Wherein, the local deformation scalar (i.e., the maximum scaling quotient) of each frame in the 3D surface sequence is: : .

[0028] Modulus of Beltrami coefficient Directly determines the scaling degree of the mapping at that point; when , This indicates that the mapping near that point is conformal (conformal), meaning that an infinitesimal circle remains a perfect circle after mapping. When At that time, that is When, it represents a mapping. This point When an infinitesimal circle at a given location is mapped to an infinitesimal ellipse over the parameter domain, This is the ratio of the length of the major axis to the length of the minor axis of the ellipse, which directly quantifies the severity of local deformation. The larger the value, the more severe the tensile or compressive deformation experienced at that point.

[0029] S400 constructs the intrinsic deformation trajectory curve of each fixed anatomical point in the unified parameter domain throughout the entire cardiac cycle based on the local deformation scalar of each frame.

[0030] Specifically, by tracking the deformation scalar change of each vertex over the entire time span of the surface sequence in a unified parameter domain, the trajectory of the deformation scalar change over time can be constructed for each fixed anatomical vertex, which is equivalent to constructing the Beltrami coefficient trajectory for the same anatomical point.

[0031] In the unified parameter domain Above, for each fixed anatomical point, extract its value from all... Local deformation scalar value at each time frame 、 By connecting these values ​​in chronological order, the deformation trajectory of that point throughout the entire cardiac cycle can be constructed: The trajectory The dynamic deformation process of the myocardial tissue at this anatomical location was fully recorded.

[0032] S500 determines the deformation characteristic vector based on the intrinsic deformation trajectory curve.

[0033] In this embodiment, for each fixed anatomical vertex or a group of fixed anatomical vertices, the corresponding deformation feature vector is a feature parameter or a group of feature parameters extracted from the trajectory and quantified to characterize the core physical properties of myocardial motion.

[0034] Among them, the deformation feature vector is the deformation feature vector, which includes features for quantifying the synchronicity of local contraction and / or features for quantifying the complexity of local motion.

[0035] The deformation eigenvectors include one or more of the following: intrinsic deformation amplitude, deformation peak time, deformation relaxation rate, deformation rhythm dominant frequency, deformation spectral entropy, deformation dynamic entropy, trajectory curvature, phase synchronization index, fractal dimension, and Lyapunov exponent.

[0036] In one specific embodiment, the deformation eigenvector preferably includes the intrinsic deformation amplitude. Deformation peak time Deformation relaxation rate Deformation rhythm dominant frequency Deformation spectrum entropy and deformation dynamics entropy In this embodiment, for each fixed anatomical point, a set of feature parameters can be extracted from the constructed deformation trajectory to form a spatiotemporal feature parameter, i.e., a deformation feature vector, used to characterize the myocardial motion deformation pattern.

[0037] The methods for obtaining the intrinsic deformation amplitude include: based on the intrinsic deformation trajectory curve. The peak and trough values ​​in the data determine the intrinsic deformation amplitude. Intrinsic deformation amplitude for: =max -min This value directly quantifies the maximum geometric stretch ratio (i.e., the ratio from maximum compression to maximum stretch) experienced by the myocardial tissue at that point during a cardiac cycle, and is an essential geometric measure of local contractile capacity.

[0038] Among them, the time to peak deformation The method for obtaining this parameter includes determining the peak deformation time based on the time from the onset of the cardiac cycle to the peak value of the intrinsic deformation amplitude. The peak value of the intrinsic deformation amplitude refers to the moment when the myocardium shortens or deforms to its maximum value in a certain direction (e.g., longitudinally). This parameter reflects the moment when the local myocardium reaches its maximum geometric extension state, and is used to accurately assess the synchronicity and coordination of regional contraction.

[0039] Among them, the variable relaxation rate The methods for obtaining this information include: identifying the rate at which the local deformation scalar during diastole decreases from its peak value from the intrinsic deformation trajectory curve, as the variable relaxation rate (…). This rate quantifies how quickly myocardial tissue recovers (relaxes) from a stretched state to a baseline state, and is a direct geometric-dynamic indicator for assessing local diastolic function.

[0040] Among them, the dominant frequency of deformation rhythm The methods for obtaining the intrinsic deformation trajectory curve include: performing spectral analysis on the intrinsic deformation trajectory curve, and using the obtained dominant frequency components as the dominant frequency of the deformation rhythm. This frequency characterizes the dominant rhythm of local geometric deformation over time, is associated with the overall cardiac cycle, but may reveal regional rhythm abnormalities.

[0041] Among them, deformation spectral entropy The methods for obtaining the entropy include: obtaining the entropy value of the power spectrum of the intrinsic deformation trajectory curve, and using it as the deformation spectrum entropy. .

[0042] Specifically, a Fourier transform is performed on the intrinsic deformation trajectory curve to decompose it into several sine waves of different frequencies. The energy (power) of each frequency component is then calculated, resulting in a power spectrum graph. The horizontal axis represents frequency, and the vertical axis represents energy intensity. This graph illustrates the distribution of energy in the deformation motion at various frequencies. The power spectrum is then treated as a probability distribution; for example, the total energy of the entire power spectrum is considered as 1, and the proportion of energy at each frequency point to the total energy is recorded as the probability of that frequency occurring. Finally, the entropy value of the power spectrum is obtained using the information entropy formula, i.e., the deformation spectrum entropy.

[0043] This value is used to assess the regularity of the rhythm. The entropy value measures the dispersion of deformation energy in the frequency domain. If the calculated entropy value is low, it indicates that the deformation rhythm is simple, regular, and orderly. If the calculated entropy value is high, it indicates that the deformation rhythm is complex, irregular, and chaotic, which may indicate electromechanical decoupling or motor disorder caused by fibrosis in a pathological state.

[0044] Among them, deformation dynamic entropy The methods for obtaining the data include: obtaining the nonlinear dynamic sample entropy of the intrinsic deformation trajectory curve, and using it as the deformation dynamic entropy. This parameter characterizes the complexity and unpredictability of deformation sequences in the time domain. It is a direct measure of the complexity of the dynamic system of myocardial tissue deformation and is sensitive to the simplification of motion patterns in heart failure, myocardial hypertrophy, and other conditions.

[0045] From the Beltrami coefficient trajectory at each point, multi-dimensional and systematic features including amplitude, time, rate, frequency, and complexity are extracted. This set of features constitutes a unique and interpretable geometric deformation fingerprint for each myocardial region, far exceeding the information capacity of a single scalar parameter (such as ejection fraction).

[0046] The left ventricular myocardial deformation quantification method disclosed in this application takes a sequence of left ventricular myocardial surfaces with stable point correspondences on a unified parameter domain as the processing object. First, based on the mapping relationship from the surface to the parameter domain, the Beltrami coefficient corresponding to each point on the surface is calculated, and its maximum stretch quotient is derived as a local deformation scalar. Then, within a complete cardiac cycle, the temporal changes of the deformation scalar are tracked point by point to construct its trajectory curve. Finally, a set of characteristic parameters representing the core physical properties of myocardial motion are extracted and quantified from the trajectory. This application constructs a fixed parameter domain as a unified geometric reference framework. By establishing a homeomorphic mapping from the myocardial surface at each time point to this common parameter domain, three-dimensional motion is encoded as the temporal evolution of Beltrami coefficients at two-dimensional parameter points. The determinism of the mapping replaces the estimation of frame-by-frame registration in traditional three-dimensional space, fundamentally and effectively avoiding corresponding point drift and cumulative errors. It can fundamentally solve the core problem of unstable correspondences of anatomical points, and ultimately achieve high-precision, interpretable, and physiologically consistent cardiac deformation analysis, that is, it can achieve accurate quantification of myocardial deformation with high spatiotemporal consistency.

[0047] In this embodiment, the three-dimensional surface sequence has no coordinates. By standardizing the target surface model, the positions of all feature points on the curve can be determined.

[0048] The two-dimensional parameter domain can be a unit topological disk, a unit topological sphere, or a planar topological rectangle. Specifically, based on the topological structure of the left ventricular myocardium, which is a zero-genus surface with a boundary, a two-dimensional parameter domain that is topologically homeomorphic to it can be defined. Typically, a unit disk can be chosen; for closed surfaces, a unit sphere can also be used; other domains such as planar rectangles are also applicable.

[0049] The method of "mapping each frame in the three-dimensional surface sequence to a unified parameter domain and obtaining the Beltrami coefficient of each fixed anatomical point in the unified parameter domain for each time frame" specifically includes: using a priori three-dimensional motion tracking or registration technique to establish a spatiotemporal vertex correspondence; and each frame in the three-dimensional surface sequence has been independently mapped to the same two-dimensional parameter domain. Then, for each anatomical position, based on the mapping relationship of its surface in each time frame, the Beltrami coefficient of its corresponding time frame is calculated respectively.

[0050] Suppose there is a point P on a surface S. By using local parameterization mapping, it is equivalent to giving it a complex structure locally (because the parameter domain is a complex plane region with natural complex coordinates). That is, by projecting (parameterizing) the point on the surface to the parameter domain, we can obtain a mapping from the surface to the plane. Using this mapping, we can transform the differential relations on the surface to the plane, and thus calculate the Beltrami coefficients on the plane to characterize the local complex structure distortion of the original surface mapping.

[0051] For mapping a three-dimensional heart surface to a two-dimensional parameter domain, it is preferable to solve the Laplace equation on the surface, i.e., to process it using the discrete Laplace-Beltrami operator based on cotangent weights, which can minimize the distortion of local angles.

[0052] Taking the unit topology disk as an example, the method for obtaining the Beltrami coefficient corresponding to each fixed dissection point in the unified parameter domain at each time frame specifically includes: 1) Identifying the boundary edges and internal edges on the surface to be mapped in each frame, determining the total length of the output boundary and the cumulative length of each fixed dissection point on the boundary, that is, identifying the boundary edges and internal edges on the triangular mesh surface to be mapped in each frame, and determining its ordered set of fixed dissection points on the boundary; based on the set of fixed dissection points on the boundary, calculating the length of each boundary edge, and accordingly calculating the cumulative total length of the entire surface boundary, the total length of the output boundary, and the cumulative arc length of each fixed dissection point on the boundary.

[0053] 2) Based on the total boundary length and the cumulative length of each fixed boundary anatomy point, map the boundary of each frame surface to the fixed boundary shape of the target unified parameter domain, obtaining the predetermined coordinates of all fixed boundary anatomy points in the unified parameter domain. For the case where the unified parameter domain is a unit disk, assign a unique central angle to each fixed boundary anatomy point based on the proportion of its cumulative arc length to the total length, calculate its two-dimensional coordinates on the unit circle, and output the predetermined coordinates of all fixed boundary anatomy points in the unified parameter domain.

[0054] 3) Based on the predetermined coordinates and the Laplace-Beltrami operator, construct a system of linear equations about the coordinates of the fixed internal anatomical points. The coefficient matrix of the linear equations is constructed by calculating the cotangent weight of each edge in the triangular mesh, and the right-hand side of the equations is determined by the known coordinates of the fixed boundary anatomical points.

[0055] 4) Solve the system of linear equations to obtain the two-dimensional coordinates of all internal fixed anatomical points in the unified parameter domain, and output the complete mapping relationship from each frame surface to the unified parameter domain.

[0056] 5) Based on the complete mapping relationship, obtain the Beltrami coefficients corresponding to each fixed anatomical point in the unified parameter domain for each time frame.

[0057] Specifically, local isothermal coordinates are introduced on the surface and the parameter domain respectively. By calculating the partial derivatives of the mapping with respect to the isothermal coordinates, a differential operator is constructed. Based on the ratio of the differential operator, the complex Beltrami coefficient μ(z) defined at each point on the surface is calculated to quantify the local angular distortion characteristics of the mapping at various points around the world.

[0058] Taking the case where the keyframe is the end-diastolic frame and the parameter domain is the unit disk as an example, firstly, a graph search algorithm is used to identify the internal and boundary edges in the triangular mesh. Let the set of boundary points of the left ventricular surface be... Then calculate the total boundary length according to the first formula. The first formula is: ,in Indicates connection to fixed anatomical points and The length of the boundary edge, where n is the total number of fixed anatomical points. Let i be the i-th fixed anatomical point. Then, calculate the central angle corresponding to each fixed anatomical point on the boundary according to the second formula. The second formula is: , ,in To start from the beginning To fixed anatomical point The cumulative boundary length, Indicates connection to fixed anatomical points and The length of the boundary edge, For the first A fixed anatomical point, For the first A fixed anatomical point.

[0059] After setting the boundary conditions, the discrete differential geometry method of the Laplace-Beltrami operator is used to calculate the coordinates of the fixed internal dissection points on the target disk using the cotangent weight.

[0060] Wherein, the cotangent weight matrix of edge (i,j) for: .in, , Let (i,j) be the two opposite angles of the edge (i,j) in two adjacent triangles. An interior edge is an edge whose two vertices are not on the boundary. , They are the edges (i, j) The two opposite angles (i.e., the angles opposite the side in the two triangles to which the side belongs). A boundary edge is an edge whose at least one vertex lies on the boundary; in this case, the edge usually belongs to only one triangle. Let be the angle opposite to a side in the triangle.

[0061] Linear system: Let the mapping f map vertex i to two-dimensional coordinates. For each internal vertex Its parameter coordinates satisfy: , Let i be the set of neighborhood points of point i, that is, satisfying and .

[0062] For boundary vertices, The boundary of the unit disk is fixed (i.e., the boundary of the unit disk is known). Solving this sparse linear system with respect to coordinates u and v yields the parametric coordinates of all internal vertices, i.e., the coordinates (u, v) of each vertex on the unit disk. Specifically, the above formula, after simplification, becomes: A·U=B u and A·U=B v Where A is an (M×M) sparse coefficient matrix (M is the number of interior points), U is the u coordinate of the interior point (to be solved), and V is the v coordinate of the interior point (to be solved). By sparsely solving the linear equation system, the parametric coordinates of all interior vertices are obtained, that is, the coordinates (u, v) of each vertex on the unit disk.

[0063] For the above mapping, its corresponding Beltrami coefficient can be calculated. This coefficient is a complex function describing the local angular distortion of the quasi-conformal mapping, and its specific calculation method is as follows: set up Left ventricular curved surface To topological disk The mapping on the surface Introducing isothermal coordinates above On the disk Introducing isothermal coordinates above And define the following differential operator: .

[0064] Treat the mapping as a complex function , In the two-dimensional parameter domain Isothermal coordinates introduced on (e.g., unit disk), Beltrami coefficient for: ,in Let be the local complex coordinates on the surface. It is a complex-valued function with a magnitude of It directly and accurately quantifies the mapping. At this point Local angular distortion at that location.

[0065] Using this method, for each frame A mapping can be found for each. This ensures that the deformation descriptors are all identical and that the boundary conditions corresponding to each frame are satisfied, i.e., the conditions of each target frame are satisfied. Boundary points mapped to a unified parameter domain The specified position on the boundary.

[0066] In another preferred embodiment, the mapping relationship between each frame in the 3D surface sequence and the unified parameter domain can be established by 3D motion tracking or registration techniques (such as non-rigid registration algorithms based on image features, biomechanical models, or deep learning) to establish an accurate spatiotemporal vertex correspondence. That is, for any given anatomical location, there exists a set of vertices { ( For fixed indexes, (for time frames), where each ∈ Furthermore, all these vertices represent the trajectory of the same myocardial anatomical point in three-dimensional space.

[0067] Furthermore, the mapping relationship between each frame in a 3D surface sequence and the unified parameter domain can be handled through harmonic mapping; harmonic mapping seeks the smoothest mapping from the 3D surface to the 2D parameter domain by minimizing the elastic energy (Dirichlet energy). Since it does not require the angles to be absolutely constant, harmonic mapping exhibits greater flexibility in handling complex boundary conditions and can find solutions that minimize the overall stretching of the mapping, making it a very fundamental parametric technique in the field of computer graphics.

[0068] Furthermore, modeling can be based on the anatomical features of the heart. For example, a B-spline active surface model based on cylindrical coordinates cleverly utilizes the approximately cylindrical geometry of the left ventricle to construct a B-spline surface in cylindrical coordinates. The isoparametric curve mesh of this model (defined by longitudinal and circumferential parameters) naturally constitutes a two-dimensional parameter space. By adjusting the control points to make the model fit the myocardial boundary in the image, segmentation and parameterization are completed simultaneously. This method has clear physical meaning of parameters and can naturally use angular coordinates to uniformly describe the deformation of the minor axis and circumferential direction, thereby simplifying the complexity of the model. Similarly, deformable NURBS (non-uniform rational B-spline) models can also be used. By flexibly adjusting the position and weight of the control points, NURBS models can accurately represent the complex free-form surface of the heart. Its node vectors themselves define a fine two-dimensional parameter space and have local modification characteristics, making it very suitable for medical image analysis and geometric transformation tasks that require high-precision geometric representation.

[0069] In this embodiment, based on stable vertex correspondence, for each fixed anatomical position (corresponding vertex set) in three-dimensional space... { | t=1,...,T} Extract it from all In each time frame, through their respective mappings f t Calculated local deformation scalar value , Connecting them in chronological order constructs the deformation trajectory of this anatomical location throughout the entire cardiac cycle: The trajectory This complete record of the dynamic process of pure local deformation of the myocardial tissue allows for a multi-dimensional and systematic characterization of the motion deformation of local myocardium, based on its intrinsic deformation trajectory. The desired feature parameters are extracted to construct the deformation feature vector of the corresponding point. Furthermore, it should be noted that any other feature parameters derived through mathematical transformation based on the same geometric object (i.e., Beltrami coefficients or their maximum stretch quotient trajectory) to characterize the physical properties of myocardial deformation are equivalent variations or simple extensions under the core principle of this invention and should fall within the protection scope of this invention.

[0070] Compared with existing technologies, this invention, based on a reliable stable spatiotemporal correspondence, achieves direct and robust quantification of left ventricular myocardial deformation by calculating the Beltrami coefficient and its maximum stretch quotient—an intrinsic geometric quantity. Furthermore, it extracts multidimensional spatiotemporal features from the deformation trajectory that systematically characterize the intensity, synchronicity, and complexity of myocardial motion. This method fundamentally avoids the inherent instability of midpoint correspondence in traditional three-dimensional registration, producing quantitative results with clear geometric and physical meaning and significant clinical diagnostic value.

[0071] Traditional methods attempt to track points in complex, dynamically changing 3D space, while this application tracks points in a fixed 2D parameter domain (like tracking a fixed coordinate on a map). By establishing a pre-defined and stable mapping from 3D surfaces of each time frame to this common 2D domain, the spatiotemporal correspondence problem is brought forward and solidified, completely eliminating the point drift and accumulated errors inherent in traditional 3D registration.

[0072] In this application, the maximum stretch quotient corresponding to the Beltrami coefficient is used as the deformation scalar. This quantity is an intrinsic geometric invariant under conformal mapping, which directly quantifies local angular distortion and area stretch ratio. It removes the interference of the overall rigid body motion (translation and rotation) of the heart and purely reflects the intrinsic deformation of myocardial tissue, with clear physical meaning.

[0073] Furthermore, referring to Figure 2The method disclosed in this application can also obtain a schematic diagram of the spatiotemporal distribution of the Beltrami coefficient field on the surface of the heart. This diagram can be used to intuitively locate the core areas of abnormal deformation (such as the interventricular septum of HCM and the right ventricular insertion point of ARVC), providing a new visual perspective for understanding the spatial heterogeneity and progression mechanism of the disease.

[0074] Secondly, this application discloses a left ventricular myocardial deformation system for performing the left ventricular myocardial deformation method disclosed in the first aspect of this application. The system includes: The 3D surface sequence module is used to obtain a dynamic 3D surface sequence of the left ventricular myocardium within a complete cardiac cycle. The mapping module is used to obtain the mapping relationship between each frame in the 3D surface sequence and the unified parameter domain, and to obtain the Beltrami coefficient corresponding to each fixed anatomical point in the unified parameter domain at each time frame based on the mapping relationship. The deformation field calculation module is used to obtain the local deformation scalar of each frame in the three-dimensional surface sequence based on the Beltrami coefficient; The trajectory construction module is used to construct the intrinsic deformation trajectory curve of each fixed anatomical point in the cardiac cycle on a unified parameter domain based on the local deformation scalar of each frame. The feature extraction module is used to determine the deformation feature vector based on the intrinsic deformation trajectory curve.

[0075] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0076] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0077] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0078] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0079] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0080] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0081] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for quantifying left ventricular myocardial deformation, characterized in that, include: Obtain a three-dimensional surface sequence of the dynamic left ventricular myocardium within a complete cardiac cycle; Obtain the mapping relationship between each frame in the three-dimensional surface sequence and the unified parameter domain, and obtain the Beltrami coefficient corresponding to each fixed anatomical point in the unified parameter domain at each time frame based on the mapping relationship; Based on the Beltrami coefficients, the local deformation scalar of each frame in the three-dimensional surface sequence is obtained; Based on the local deformation scalar of each frame, construct the intrinsic deformation trajectory curve of each fixed anatomical point in the unified parameter domain during the cardiac cycle; The deformation feature vector is determined based on the intrinsic deformation trajectory curve.

2. The method for left ventricular myocardial deformation quantification according to claim 1, characterized in that, The unified parameter domain is a two-dimensional parameter domain; In the three-dimensional surface sequence, different frames representing the same anatomical location have the same fixed anatomical point coordinates on the unified parameter domain.

3. The method for left ventricular myocardial deformation quantification according to claim 1, characterized in that, The local deformation scalar of each frame in the three-dimensional surface sequence is: : ; Let be the Beltrami coefficient.

4. The method for left ventricular myocardial deformation quantification according to claim 1, characterized in that, The deformation eigenvector includes intrinsic deformation amplitude and deformation peak time; Determining the deformation feature vector based on the intrinsic deformation trajectory curve includes: The intrinsic deformation amplitude is determined based on the peak and valley values ​​in the intrinsic deformation trajectory curve. The time to peak deformation is determined based on the time from the start of the cardiac cycle to the time when the intrinsic deformation amplitude reaches its peak value.

5. The method for left ventricular myocardial deformation quantification according to claim 1, characterized in that, The deformation feature vector also includes the deformation relaxation rate; The step of determining the deformation feature vector based on the intrinsic deformation trajectory curve includes: identifying the rate at which the local deformation scalar during diastole decreases from its peak value from the intrinsic deformation trajectory curve, as the variable relaxation rate.

6. The method for left ventricular myocardial deformation quantification according to claim 1, characterized in that, The deformation feature vector also includes the dominant frequency of the deformation rhythm; The step of determining the deformation feature vector based on the intrinsic deformation trajectory curve includes: performing spectral analysis on the intrinsic deformation trajectory curve and taking the obtained dominant frequency component as the dominant frequency of the deformation rhythm.

7. The method for left ventricular myocardial deformation quantification according to claim 1, characterized in that, The deformation feature vector also includes deformation spectral entropy; The step of determining the deformation feature vector based on the intrinsic deformation trajectory curve includes: obtaining the entropy value of the power spectrum of the intrinsic deformation trajectory curve and using it as the deformation spectrum entropy.

8. The method for left ventricular myocardial deformation quantification according to claim 7, characterized in that, The deformation feature vector also includes deformation dynamic entropy; The step of determining the deformation feature vector based on the intrinsic deformation trajectory curve includes: obtaining the nonlinear dynamic sample entropy of the intrinsic deformation trajectory curve and using it as the deformation dynamic entropy.

9. The method for left ventricular myocardial deformation quantification according to claim 1, characterized in that, The deformation eigenvectors include one or more of the following: intrinsic deformation amplitude, deformation peak time, deformation relaxation rate, deformation rhythm dominant frequency, deformation spectral entropy, deformation dynamic entropy, trajectory curvature, phase synchronization index, and Lyapunov index.

10. A system for customizing left ventricular myocardial deformation, characterized in that, include: The 3D surface sequence module is used to obtain a dynamic 3D surface sequence of the left ventricular myocardium within a complete cardiac cycle. The mapping module is used to obtain the mapping relationship between each frame in the three-dimensional surface sequence and the unified parameter domain, and to obtain the Beltrami coefficient corresponding to each fixed anatomical point in the unified parameter domain at each time frame based on the mapping relationship. The deformation field calculation module is used to obtain the local deformation scalar of each frame in the three-dimensional surface sequence based on the Beltrami coefficients. The trajectory construction module is used to construct the intrinsic deformation trajectory curve of each fixed anatomical point in the unified parameter domain during the cardiac cycle based on the local deformation scalar of each frame. The feature extraction module is used to determine the deformation feature vector based on the intrinsic deformation trajectory curve.

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