{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T16:43:12Z","timestamp":1793032992476,"version":"4.5.0"},"reference-count":10,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"vor","delay-in-days":26,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,5]]},"abstract":"Abstract<\/jats:title>\n \n Purpose<\/jats:title>\n The Elekta Unity MR\u2010Linac enables online adaptive radiotherapy (oART) with integrated 1.5T MRI. Given its relatively low dose rate, identifying optimal IMRT planning parameters is essential to achieve efficient delivery. This study investigates parameter optimization strategies that enhance delivery efficiency while maintaining high dosimetric quality.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A dosimetric study was designed using ten prostate simultaneous integrated boost (SIB) patients (45\u00a0Gy\/40\u00a0Gy in five fractions) and 10 liver SBRT patients (42\u201354\u00a0Gy in 3\u20135 fractions). Each patient has three plans: (1) Clinical, (2) Refa (segment\u2010limited, \u2264120 segments), and (3) Refb (MU\u2010modulated plan with larger minimum segment width (0.7\u00a0cm or 1\u00a0cm) and higher fluence map smoothing level (medium or high)). A linear equation for estimating delivery time was established and validated against 1173 retrospective Unity plans based on total segments and Monitor Units (MUs). All the study plans were evaluated by comparing delivery efficiency related metrics (total number of segments, MUs, estimated delivery time), and plan quality using dose\u2013volume histogram (DVH) metrics for targets and organs\u2010at\u2010risk (OARs). Additionally, an online adaptive study was performed on representative cases: the three reference plans were adapted to a daily image, and the resulting adaptive plans were assessed for optimization time and plan quality against clinical criteria.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Using the derived delivery time estimation equation on 173 unseen test cases, the mean absolute error (MAE) between estimated and recorded times was 0.9 min, with 84% of cases with a prediction error within \u00b11 min, and with 95% of cases falling within \u00b12 min. Both strategies reduced delivery complexity compared with clinical plans. For prostate cases, Refa and Refb lowered segment counts to <120 (vs. original 137\u2013178) and reduced MUs from 3022 to 2899 (Refa) and 2497 (Refb), respectively. Delivery times decreased on average by 2.6 min with Refa (up to 5.4) and 4.0 min with Refb (up to 6.7). For liver cases, segment counts dropped from (149\u2013198) to <120, with mean MUs reduced from 4233 to 3528 (Refa) and 3309 (Refb). Delivery times were shortened from 24.4 min to 18.9 (Refa) and 18.3 (Refb), up to 7.6 min saving for Refa and up to 8.9 min for Refb. Across both cohorts, PTV coverage and OAR sparing were maintained, with no statistically significant differences (\n p<\/jats:italic>\n \u00a0>\u00a00.05). All adaptive plans from Refa and Refb were completed within 5\u201313 min, consistent with standard clinical online optimization times. After normalization to the clinical adaptive plan, all plans met the dosimetric goals. A similar time\u2010saving trend was observed across the adaptive plans.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Systematic adjustment of Unity IMRT planning parameters\u2014specifically limiting total segment number, reasonable minimum segment width, and fluence smoothing\u2014can markedly improve delivery efficiency while maintaining clinically acceptable dosimetric quality. These findings provide practical, evidence\u2010based guidelines for parameter selection in Unity planning, supporting reduced treatment times, improved patient throughput, and broader clinical feasibility of MR\u2010guided online adaptive radiotherapy.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/acm2.70638","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T04:45:03Z","timestamp":1779943503000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Strategies for enhancing delivery efficiency on MR\u2010Linac: A dosimetric study and historical plan review"],"prefix":"10.1002","volume":"27","author":[{"given":"Ying","family":"Zhang","sequence":"first","affiliation":[{"name":"Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Tang","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Kabat","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Visak","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsuicheng","family":"Chiu","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiqi","family":"Li","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve","family":"Jiang","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mu\u2010Han","family":"Lin","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,27]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ctro.2019.04.001"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21707"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2020.09.026"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2020.07.003"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmrs.712"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2024.110513"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2022.05.039"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.06.044"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ab52ce"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.70005"}],"container-title":["Journal of Applied Clinical Medical 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