Noninvasive Cardiac Imaging via Low-Rank Sparse Constraints
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Solution Overview
Problem
Current non-invasive cardiac electrophysiological imaging methods, such as electrocardiography, lack accuracy in locating specific cardiac abnormalities due to their reliance on rough mappings of body surface potential, which cannot accurately reconstruct the complex spatio-temporal distribution of endocardial and epicardial potentials.
Innovation Solution
A non-invasive imaging method utilizing low-rank and sparse constraints to decompose the spatiotemporal distribution of cardiac potentials into a smooth background and detailed components, reconstructing the endocardial and epicardial potential distribution through a 3D heart-torso model and quasi-static electric field modeling, with preprocessing and inverse problem solving via boundary element methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional electrocardiography is used for non-invasive cardiac imaging, then safety and convenience are improved, but measurement precision and diagnostic accuracy deteriorate due to rough mapping of body surface potential
Solution Approach 1:
The patent changes the mathematical parameters and constraints used in inverse problem solving. It introduces low-rank constraints to exploit temporal correlations and sparse constraints to exploit spatial sparsity, transforming the single-parameter Tikhonov regularization into a multi-parameter optimization framework that simultaneously enforces temporal smoothness and spatial sparsity, thereby improving measurement precision while maintaining non-invasive convenience
Solution Approach 2:
The patent combines multiple constraint types (low-rank temporal constraints and sparse spatial constraints) into a composite regularization framework. This composite approach integrates the strengths of different mathematical constraints to simultaneously capture temporal evolution patterns and spatial distribution characteristics of cardiac potentials, resolving the contradiction between ease of operation and measurement precision
2Device complexity
If single parameter Tikhonov regularization is used to solve inverse problem, then computational simplicity is improved, but measurement precision deteriorates due to reliance on single prior condition
Solution Approach 1:
The patent segments the regularization constraints into distinct components: low-rank temporal constraints that capture time-evolution patterns and sparse spatial constraints that capture space-distribution patterns. This segmentation allows each constraint to specialize in capturing specific characteristics of cardiac potentials, improving measurement precision while maintaining computational tractability through modular optimization
Solution Approach 2:
The patent adds temporal dimension to the traditional spatial-only regularization by introducing low-rank constraints that exploit temporal correlations across multiple cardiac cycles. This dimensional extension transforms the problem from static spatial reconstruction to dynamic spatio-temporal reconstruction, significantly improving measurement precision for arrhythmia localization
3Stability of the object's composition
If spatial smoothness constraint is applied alone, then solution stability is improved, but measurement precision deteriorates due to inability to capture complex spatio-temporal distribution characteristics
Solution Approach 1:
The patent introduces dynamic temporal constraints that model the time-evolution of cardiac potentials through low-rank structure enforcement. This dynamic approach captures the physiological reality that cardiac potentials evolve smoothly over time within each cardiac cycle, adding temporal stability to the spatial smoothness constraint and improving overall measurement precision for moving arrhythmia foci
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides a more accurate and intuitive reconstruction of cardiac potential distributions, enabling precise localization and characterization of lesion areas, enhancing diagnostic accuracy beyond traditional non-invasive methods.
Implementation Method 1
establishing a quasi-static electric field model of the heart-trunk and solving the electric field model by using the boundary element method to calculate the positive problem of the ECG
Data Source
AI summary
The present invention discloses a method for noninvasive imaging of cardiac electrophysiological based on low rank and sparse constraints. This method decomposes the spatio-temporal distribution of endocardial and epicardial potentials into a low-rank matrix representing smooth potential components and a sparse matrix representing the details of potential salience according to the prior condition of spatio-temporal correlation of the endocardial and epicardial potential distribution of the heart. By introducing low rank and sparse constraints, the solution of the ill-conditioned inverse problem of ECG is constrained to the unique optimal solution. The invention combines the individualized three-dimensional heart model of the subject to obtain a three-dimensional dynamic distribution image of the cardiac endocardial and epicardial potential of the subject, which has important practical application value.