{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T03:11:37Z","timestamp":1790392297020,"version":"4.1.0"},"reference-count":37,"publisher":"IOP Publishing","issue":"8","license":[{"start":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T00:00:00Z","timestamp":1712620800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T00:00:00Z","timestamp":1712620800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2024,4,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    In this study, we tackle the challenge of latency in magnetic resonance linear accelerator (MR-Linac) systems, which compromises target coverage accuracy in gated real-time radiotherapy. Our focus is on enhancing motion prediction precision in abdominal organs to address this issue. We developed a convolutional long short-term memory (convLSTM) model, utilizing 2D cine magnetic resonance (cine-MR) imaging for this purpose.\n                    <jats:italic>Approach.<\/jats:italic>\n                    Our model, featuring a sequence-to-one architecture with six input frames and one output frame, employs structural similarity index measure (SSIM) as loss function. Data was gathered from 17 cine-MRI datasets using the Philips Ingenia MR-sim system and an Elekta Unity MR-Linac equivalent sequence, focusing on regions of interest (ROIs) like the stomach, liver, pancreas, and kidney. The datasets varied in duration from 1 to 10 min.\n                    <jats:italic>Main results.<\/jats:italic>\n                    The study comprised three main phases: hyperparameter optimization, individual training, and transfer learning with or without fine-tuning. Hyperparameters were initially optimized to construct the most effective model. Then, the model was individually applied to each dataset to predict images four frames ahead (1.24\u20133.28 s). We evaluated the model\u2019s performance using metrics such as SSIM, normalized mean square error, normalized correlation coefficient, and peak signal-to-noise ratio, specifically for ROIs with target motion. The average SSIM values achieved were 0.54, 0.64, 0.77, and 0.66 for the stomach, liver, kidney, and pancreas, respectively. In the transfer learning phase with fine-tuning, the model showed improved SSIM values of 0.69 for the liver and 0.78 for the kidney, compared to 0.64 and 0.37 without fine-tuning.\n                    <jats:italic>Significance<\/jats:italic>\n                    . The study\u2019s significant contribution is demonstrating the convLSTM model\u2019s ability to accurately predict motion for multiple abdominal organs using a Unity-equivalent MR sequence. This advancement is key in mitigating latency issues in MR-Linac radiotherapy, potentially improving the precision and effectiveness of real-time treatment for abdominal cancers.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad3722","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T18:44:00Z","timestamp":1711133040000},"page":"085024","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Convolutional LSTM model for cine image prediction of abdominal motion"],"prefix":"10.1088","volume":"69","author":[{"given":"J","family":"Weng","sequence":"first","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S H V","family":"Bhupathiraju","sequence":"additional","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T","family":"Samant","sequence":"additional","affiliation":[{"name":"Tera Insights, Gainesville, FL,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A","family":"Dresner","sequence":"additional","affiliation":[{"name":"Philips Healthcare MR Oncology, Cleveland, OH,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S S","family":"Samant","sequence":"additional","affiliation":[{"name":"University of Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"pmbad3722bib1","doi-asserted-by":"publisher","first-page":"223","DOI":"10.3978\/j.issn.2078-6891.2014.028","type":"journal-article","article-title":"Motion management in gastrointestinal cancers","volume":"5","author":"Abbas","year":"2014","journal-title":"J. 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