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Nonlocal based FISTA network for noninvasive cardiac transmembrane potential imaging.
Ran, Ao; Cheng, Linsheng; Xie, Shuting; Liu, Muqing; Pu, Cailing; Hu, Hongjie; Liu, Huafeng.
Afiliación
  • Ran A; State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, People's Republic of China.
  • Cheng L; State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, People's Republic of China.
  • Xie S; State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, People's Republic of China.
  • Liu M; State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, People's Republic of China.
  • Pu C; Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, People's Republic of China.
  • Hu H; Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, People's Republic of China.
  • Liu H; State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, People's Republic of China.
Phys Med Biol ; 69(7)2024 Mar 21.
Article en En | MEDLINE | ID: mdl-38417179
ABSTRACT
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Corazón / Cardiopatías Límite: Humans Idioma: En Revista: Phys Med Biol Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Corazón / Cardiopatías Límite: Humans Idioma: En Revista: Phys Med Biol Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido