{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T19:00:09Z","timestamp":1772391609836,"version":"3.50.1"},"reference-count":52,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T00:00:00Z","timestamp":1743811200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82001928"],"award-info":[{"award-number":["82001928"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Four\u2010dimension computed tomography (4D\u2010CT) provides important respiration\u2010related information for thoracic radiotherapy. Its quality is challenged by various respiratory patterns. Its acquisition gives rise to the risk of higher radiation exposure. Based on a continuously estimated deformation, a 4D synthesis by warping a high\u2010quality volumetric image is a possible solution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To propose a non\u2010patient\u2010specific cascaded ensemble model (CEM) to estimate respiration\u2010induced thoracic tissue deformation from surface motion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The CEM is cascaded by three deep learning\u2010based models. By inputting the surface motion, CEM outputs a deformation vector field (DVF) inside thorax. In our work, the surface motion was simulated using the body contours derived from 4D\u2010CT. The CEM was trained on our private database including 62 4D\u2010CT sets, and was tested on a public database encompassing 80 4D\u2010CT sets. To evaluate CEM, we employed the model output DVF to generate a few series of synthesized CTs, and compared them with the ground truth. CEM was also compared with other published works.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>CEM synthesized CT with an mRMSE (average root mean square error) of 61.06\u00a0\u00b1\u00a010.43HU (average\u00a0\u00b1\u00a0standard deviation), an mSSIM (average structural similarity index measure) of 0.990\u00a0\u00b1\u00a00.004, and an mMAE (average mean absolute error) of 26.80\u00a0\u00b1\u00a05.65HU. Compared with other works, CEM showed the best result.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The results demonstrated the effectiveness of CEM on estimating tissue DVF inside thorax. CEM requires no patient\u2010specific breathing data sampling and no additional training before treatment. It shows potential for broad applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17804","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T22:28:25Z","timestamp":1743892105000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning\u2010based estimation of respiration\u2010induced deformation from surface motion: A proof\u2010of\u2010concept study on 4D thoracic image synthesis"],"prefix":"10.1002","volume":"52","author":[{"given":"Jie","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiation Physics Zhejiang Cancer Hospital  Hangzhou Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Radiation Physics Zhejiang Cancer Hospital  Hangzhou Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoping","family":"Shan","sequence":"additional","affiliation":[{"name":"Department of Radiation Physics Zhejiang Cancer Hospital  Hangzhou Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,4,5]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2021.10.147"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.12.041"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14023"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-021-01973-5"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2021.09.005"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2349696"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-017-0835-7"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1639993"},{"key":"e_1_2_8_10_1","unstructured":"CaoY\u2010H JaouenV BourbonneV et\u00a0al.Patient\u2010specific 4DCT respiratory motion synthesis using tumor\u2010aware GANs. 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