Fast fourier transform based spectral features for machine learning prediction of gamma passing rates in virtual VMAT PSQA: A feasibility study.
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| Title: | Fast fourier transform based spectral features for machine learning prediction of gamma passing rates in virtual VMAT PSQA: A feasibility study. |
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| Authors: | Yan B; School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei, Anhui, China.; Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China., Peng H; School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei, Anhui, China., Xue X; Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China., Zheng C; School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei, Anhui, China., Wu A; Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China. |
| Source: | Journal of applied clinical medical physics [J Appl Clin Med Phys] 2026 Sep; Vol. 27 (9), pp. e70772. |
| 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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