Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily.

Diffusion Model-based FOD Restoration from High Distortion in dMRI.

Fiber orientation distributions (FODs) is a popular model to represent the diffusion MRI (dMRI) data. However, imaging artifacts such as susceptibility-induced distortion in dMRI can cause signal loss and lead to the corrupted reconstruction of FODs, which prohibits successful fiber tracking and connectivity analysis in affected brain regions such as the brain stem. Generative models, such as the diffusion models, have been successfully applied in various image restoration tasks.

Automated Mapping of Residual Distortion Severity in Diffusion MRI.

Susceptibility-induced distortion is a common artifact in diffusion MRI (dMRI), which deforms the dMRI locally and poses significant challenges in connectivity analysis. While various methods were proposed to correct the distortion, residual distortions often persist at varying degrees across brain regions and subjects. Generating a voxel-level residual distortion severity map can thus be a valuable tool to better inform downstream connectivity analysis.