Strategies for enhancing delivery efficiency on MR-Linac: A dosimetric study and historical plan review.
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| Title: | Strategies for enhancing delivery efficiency on MR-Linac: A dosimetric study and historical plan review. |
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| Authors: | Zhang Y; Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA., Tang S; Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA., Kabat C; Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA., Visak J; Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA., Chiu T; Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA., Li R; Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA., Jiang S; Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA., Lin MH; Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA. |
| Source: | Journal of applied clinical medical physics [J Appl Clin Med Phys] 2026 May; Vol. 27 (5), pp. e70638. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Wiley on behalf of American Association of Physicists in Medicine Country of Publication: United States NLM ID: 101089176 Publication Model: Print Cited Medium: Internet ISSN: 1526-9914 (Electronic) Linking ISSN: 15269914 NLM ISO Abbreviation: J Appl Clin Med Phys Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1526-9914 |
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| DOI: | 10.1002/acm2.70638 |
