{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:11:29Z","timestamp":1778778689119,"version":"3.51.4"},"reference-count":56,"publisher":"IOP Publishing","issue":"7","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Scientific Research Fund of Zhejiang University","award":["No:XY2023033"],"award-info":[{"award-number":["No:XY2023033"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U1809204"],"award-info":[{"award-number":["U1809204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Talent Program of Zhejiang Province","award":["2021R51004"],"award-info":[{"award-number":["2021R51004"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2024,4,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . 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.\n                    <jats:italic>Approach<\/jats:italic>\n                    . 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.\n                    <jats:italic>Main results<\/jats:italic>\n                    . 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.\n                    <jats:italic>Significance<\/jats:italic>\n                    . 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.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad2e6d","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T17:44:45Z","timestamp":1709142285000},"page":"075018","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Nonlocal based FISTA network for noninvasive cardiac transmembrane potential imaging"],"prefix":"10.1088","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0182-3741","authenticated-orcid":true,"given":"Ao","family":"Ran","sequence":"first","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linsheng","family":"Cheng","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuting","family":"Xie","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cailing","family":"Pu","sequence":"additional","affiliation":[{"name":"Zhejiang University School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjie","family":"Hu","sequence":"additional","affiliation":[{"name":"Zhejiang University School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huafeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"pmbad2e6dbib1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/TMI.2018.2865356","type":"journal-article","article-title":"Modl: model based deep learning architecture for inverse problems","volume":"38","author":"Aggarwal","year":"2017","journal-title":"IEEE Trans. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2023-04-23","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-02-28","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-03-21","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}