Simulation-free workflow for lattice radiation therapy using deep learning predicted synthetic computed tomography: A feasibility study.
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| Title: | Simulation-free workflow for lattice radiation therapy using deep learning predicted synthetic computed tomography: A feasibility study. |
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| Authors: | Zhu L; Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA., Yu NY; Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA., Ahmed SK; Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA., Ashman JB; Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA., Toesca DS; Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA., Grams MP; Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA., Deufel CL; Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA., Duan J; Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, USA., Chen Q; Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA., Rong Y; Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA. |
| Source: | Journal of applied clinical medical physics [J Appl Clin Med Phys] 2025 Jul; Vol. 26 (7), pp. e70137. Date of Electronic Publication: 2025 Jun 12. |
| 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-Electronic 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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