Uncertainty-aware MR-based CT synthesis for robust proton therapy planning of brain tumour
The enhanced framework incorporates 3D uncertainty prediction and generates high-quality sCTs from MR images. The framework also facilitates conditioned robust optimisation, bolstering proton plan robustness against network prediction errors. The innovative feature of uncertainty visualisation and robust analyses contribute to evaluating sCT clinical utility for individual patients.
Feb 1, 2024
A Unified Generation-Registration Framework for Improved MR-based CT Synthesis in Proton Therapy
In this study, we developed an approach that substantially improves the anatomical accuracy of synthetic CT (sCT) images for MR-based radiotherapy planning. By integrating image generation with the deformable registration processes, the novel framework effectively addresses the inherent challenges in MR-CT registration and sCT evaluation. The methodological synergy of generation and registration networks, coupled with a strategic approach to proton dose planning, contributes to enhancing treatment planning accuracy. This study represents a meaningful step forward in the application of MR-based proton treatment planning and daily adaptive replanning, offering potential improvements in reducing the imaging-related dose and patient outcomes in the field of radiotherapy.
Jan 20, 2024
Our abstract is selected as a Mini-Oral presentation by ESTRO 2023!
Uncertainty-aware MR-base CT synthesis for robust proton planning of skull-based tumour
Dec 20, 2022