Cardiac Transmembrane Potential Imaging with GCN Based Iterative Soft Threshold Network

Mu, Lide; Liu, Huafeng

doi:10.1007/978-3-030-87231-1_53

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…In Ghimire et al (2018), a model based on bayesian optimization was proposed for the analysis of TMP, and later they used a generative variational auto-encoder (VAE) (Dhamala et al 2020) to improve the reconstruction density and accuracy. Considering the non-Euclidean space data of TMP, graph convolution neural network was devoted to reconstruct the distribution of TMP (Mu and Liu 2021). There are also graph-based methods thriving in non-Euclidean spatial data modeling (Dhamala et al 2019, Jiang et al 2020.…”

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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