SynthRAD2025 challenge shows AI improves synthetic CT for radiotherapy

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

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]

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• Computed Tomography
• Deep Learning
• SynthRAD2025
• Viktor Rogowski
• Cone-Beam CT
• Radiotherapy

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