Fast fourier transform based spectral features for machine learning prediction of gamma passing rates in virtual VMAT PSQA: A feasibility study.

Yan B, Peng H, Xue X, Zheng C, Wu A

DOI: 10.1002/acm2.70772
Published: 2026 Sep
Container: Journal of applied clinical medical physics
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1002/acm2.70772,
  title = {Fast fourier transform based spectral features for machine learning prediction of gamma passing rates in virtual VMAT PSQA: A feasibility study.},
  author = {Yan B and Peng H and Xue X and Zheng C and Wu A},
  year = {2026},
  journal = {Journal of applied clinical medical physics},
  doi = {10.1002/acm2.70772},
  url = {https://doi.org/10.1002/acm2.70772}
}

RIS

TY  - JOUR
TI  - Fast fourier transform based spectral features for machine learning prediction of gamma passing rates in virtual VMAT PSQA: A feasibility study.
AU  - Yan B
AU  - Peng H
AU  - Xue X
AU  - Zheng C
AU  - Wu A
PY  - 2026
JO  - Journal of applied clinical medical physics
DO  - 10.1002/acm2.70772
UR  - https://doi.org/10.1002/acm2.70772
ER  -

APA

B, Y., H, P., X, X., C, Z., & A, W. (2026). Fast fourier transform based spectral features for machine learning prediction of gamma passing rates in virtual VMAT PSQA: A feasibility study.. Journal of applied clinical medical physics. https://doi.org/10.1002/acm2.70772

Source records

pubmed · retrieved 2026-09-25T01:50:28.448Z