Noninvasive Imaging of Epicardial and Endocardial Potentials With Low Rank and Sparsity Constraints

Fang, Lin; Xu, Jingjia; Hu, Hongjie; Chen, Yunmei; Shi, Peng; Wang, Linwei; Liu, Huafeng

doi:10.1109/tbme.2019.2894286

Cited by 9 publications

(4 citation statements)

References 40 publications

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“…We focus on comparing the performance between the graph‐TV method and the classic TV method. The TV method we choose is Iteratively Re‐weighted Minimisation of TV (IRTV) [10], which has been used to solve the myocardial TMP inverse problem.…”

Section: Methodsmentioning

confidence: 99%

“…If the whole sequence is to be solved, the computational time will increase linearly with the length of the sequence. The low rank and sparse constraint assume that the solution is composed of low‐rank background and sparse foreground [10]. This method treats the solution of the whole sequence as a matrix, which takes into account the time dependence of the solution.…”

Section: Introductionmentioning

confidence: 99%

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Non‐invasive reconstruction of dynamic myocardial transmembrane potential with graph‐based total variation constraints

Xie

Wang

Zhang

et al. 2019

Healthcare Technology Letters

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Non-invasive reconstruction of electrophysiological activity in the heart is of great significance for clinical disease prevention and surgical treatment. The distribution of transmembrane potential (TMP) in three-dimensional myocardium can help us diagnose heart diseases such as myocardial ischemia and ectopic pacing. However, the problem of solving TMP is ill-posed, and appropriate constraints need to be added. The existing state-of-art method total variation minimisation only takes advantage of the local similarity in space, which has the problem of over-smoothing, and fails to take into account the relationship among frames in the dynamic TMP sequence. In this work, the authors introduce a novel regularisation method called graph-based total variation to make up for the above shortcomings. The graph structure takes the TMP value of a time sequence on each heart node as the criterion to establish the similarity relationship among the heart. Two sets of phantom experiments were set to verify the superiority of the proposed method over the traditional constraints: infarct scar reconstruction and activation wavefront reconstruction. In addition, experiments with ten real premature ventricular contractions patient data were used to demonstrate the accuracy of the authors' method in clinical applications.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non‐invasive reconstruction of dynamic myocardial transmembrane potential with graph‐based total variation constraints

Xie

Wang

Zhang

et al. 2019

Healthcare Technology Letters

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“…We realize that some efforts to reconstruct endocardial and epicardial potential (EEP) (Erik et al 2017, Fang et al 2019b from body surface ECGs are related to our work. Notable methods include the use of Twomey regularization (Twomey 1963), low rank and sparse decomposition (LSD) framework (Fang et al 2019a), and nonlocal total variation regularization (Mu and Liu 2020). Recently, some authors have employed a novel dataadaptive regression framework (Onak et al 2021) to obtain a mapping from BSP to EEP without redundant parameters.…”

Section: Introductionmentioning

confidence: 99%

Nonlocal based FISTA network for noninvasive cardiac transmembrane potential imaging

Ran,

Cheng,

Xie

et al. 2024

Phys. Med. Biol.

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Objective: The primary aim of our study is to advance our understanding and diagnosis of cardiac diseases. We focus on the reconstruction of myocardial transmembrane potential (TMP) from body surface potential mapping.Approach: We introduce a novel methodology for the reconstruction of the dynamic distribution of TMP. This is achieved through the integration of convolutional neural networks with conventional optimization algorithms. Specifically, we utilize the subject-specific transfer matrix to describe the dynamic changes in TMP distribution and ECG observations at the body surface. To estimate the TMP distribution, we employ LNFISTA-Net, a learnable non-local regularized iterative shrinkage-thresholding network. The coupled estimation processes are iteratively repeated until convergence.Main results: Our experiments demonstrate the capabilities and benefits of this strategy. The results highlight the effectiveness of our approach in accurately estimating the TMP distribution, thereby providing a reliable method for the diagnosis of cardiac diseases.Significance: Our approach demonstrates promising results, highlighting its potential utility for a range of applications in the medical field. By providing a more accurate and dynamic reconstruction of TMP, our methodology could significantly improve the diagnosis and treatment of cardiac diseases, thereby contributing to advancements in healthcare.

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“…Common invasive examinations include 16 endocardial and epicardial labeling, and 17 intracavitary electrocardiography [3] . The main System [4] and the Ensite System [5] , which are 21 comparatively higher experience for 24 physicians [7] .…”

mentioning

confidence: 99%

Ex Vivo Noninvasive Electrophysiologic Imaging Based on Cardiac Electrophysiology Simulation and Clinical Data

Chang,

Dong,

Fan

et al. 2024

Preprint

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Background: The risk stratification and prognosis of cardiac arrhythmia depend on the individual condition of patients, while invasive diagnostic methods may be risky to patient health, and current non-invasive diagnostic methods are applicable to few disease types without sensitivity and specificity. Cardiac electrophysiologic imaging technology reflects cardiac activities accurately and non-invasively, which is of great significance for the diagnosis and treatment of cardiac diseases. Methods: A complete three-dimensional bidomain cardiac electrophysiologic activity model was constructed, and simulated electrocardiogram data were obtained as training samples. Particle swarm optimization-back propagation neural network, convolutional neural network, and long short-term memory network were used respectively to reconstruct the cardiac surface potential. Results: The conduction sequence of simulation model is basically consistent with that of human heart. The P waves, PR intervals, QRS complex, and T waves in the simulated waveforms were within the normal clinical range, and the distribution trend of the simulated body surface potential mapping was consistent with the clinical data. The coefficient of determination R2 between the reconstruction results of all the algorithms and the true value is above 0.73, and the mean absolute error is below 14 mV, among which the R2 of long short-term memory network is about 0.97 and the mean absolute error about 1.2 mV. Conclusions: The electrophysiologic model constructed in this study can reflect the cardiac electrical activity, and basically contains the mapping relationship between the cardiac potential and the body surface potential. In cardiac potential reconstruction, long short-term memory network has significant advantages over other algorithms.

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Noninvasive Imaging of Epicardial and Endocardial Potentials With Low Rank and Sparsity Constraints

Cited by 9 publications

References 40 publications

Non‐invasive reconstruction of dynamic myocardial transmembrane potential with graph‐based total variation constraints

Non‐invasive reconstruction of dynamic myocardial transmembrane potential with graph‐based total variation constraints

Nonlocal based FISTA network for noninvasive cardiac transmembrane potential imaging

Ex Vivo Noninvasive Electrophysiologic Imaging Based on Cardiac Electrophysiology Simulation and Clinical Data

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