The SynthRAD2025 challenge report details advancements in generating synthetic computed tomography (sCT) images for radiotherapy planning. This year's challenge focused on converting MRI or cone-beam CT (CBCT) into CT-equivalent images, with methods evaluated on over 2,300 patient cases across different body regions. While deep learning models showed significant improvements, particularly for CBCT-to-CT conversion, challenges remain in MRI-to-CT accuracy, especially for dose-based validation. AI
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IMPACT AI-driven synthetic CT generation shows promise for improving radiotherapy planning and reducing patient exposure, though dose-based validation remains a key area for development.
RANK_REASON The cluster reports on a challenge and its results, presented in a scientific paper, evaluating AI methods for medical image generation. [lever_c_demoted from research: ic=1 ai=1.0]