ESTRO 2025 - Abstract Book

S2997

Physics - Image acquisition and processing

ESTRO 2025

Conclusion: This investigation demonstrates that PCCT improves the RED and SPR prediction accuracy in the presence of iodine based CA and enhances SPR prediction stability across varying phantom sizes. The improved reliability of SPR prediction may enable treatment planning for proton therapy based on contrast-enhanced PCCT scans.

Keywords: Photon-counting CT, SPR prediction, contrast agent

References: [1] A. P. Sauter et al. , “Dual-layer spectral computed tomography: Virtual non-contrast in comparison to true non contrast images,” Eur. J. Radiol. , vol. 104, pp. 108–114, Jul. 2018, doi: 10.1016/j.ejrad.2018.05.007. [2] S. Yamada et al. , “Radiotherapy treatment planning with contrast-enhanced computed tomography: feasibility of dual-energy virtual unenhanced imaging for improved dose calculations,” Radiat. Oncol. , vol. 9, no. 1, p. 168, Dec. 2014, doi: 10.1186/1748-717X-9-168.

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Mini-Oral Global and Local Feature Fusion Diffusion Model for Low-Dose CT Image Denoising Zhenhao Li, yue zou, xiaojie ying, Ziwei Li, Jiazhou Wang, Weigang Hu Radiation Oncology, Fudan University of Shanghai cancer center, Shanghai, China

Purpose/Objective: Low-dose computed tomography (LDCT) can reduce the radiation dose accumulation of patients during adaptive radiotherapy. However, severe noise and artifacts in LDCT images will hinder subsequent clinical diagnosis and analysis. Therefore, effectively removing noise from LDCT images has become a key step to improving the accuracy and effect of adaptive radiotherapy. Some works have begun to try to use diffusion models for low-dose CT