View in EDS

Parametric regression model approach for CT-based prediction of stopping power ratio for a Hounsfield look-up table.

Saved in:
Bibliographic Details
Title: Parametric regression model approach for CT-based prediction of stopping power ratio for a Hounsfield look-up table.
Authors: Yagi M; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.; Department of Radiation Oncology, The University of Osaka Graduate School of Medicine, Osaka, Japan., Calvin Koh WY; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore., Lew KS; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore., Chua CGA; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore., Yeap PL; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.; Department of Oncology, University of Cambridge, Cambridge, United Kingdom., Wibawa A; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore., Master Z; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore., Lee JCL; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.; Division of Physics and Applied Physics, School of Physical and Mathematical Science, Nanyang Technological University, Singapore, Singapore., Park SY; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.; Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore., Tan HQ; Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.; Division of Physics and Applied Physics, School of Physical and Mathematical Science, Nanyang Technological University, Singapore, Singapore.; Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore.
Source: Biomedical physics & engineering express [Biomed Phys Eng Express] 2026 Jun 18; Vol. 12 (3). Date of Electronic Publication: 2026 Jun 18.
Publication Type: Journal Article
Journal Info: Publisher: IOP Publishing Ltd Country of Publication: England NLM ID: 101675002 Publication Model: Electronic Cited Medium: Internet ISSN: 2057-1976 (Electronic) Linking ISSN: 20571976 NLM ISO Abbreviation: Biomed Phys Eng Express Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2057-1976
DOI:10.1088/2057-1976/ae7575