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A single platform can integrate MR-Linac DWI post-processing with traceable, expert-evaluated clinical interpretation, while highlighting the safeguards needed to verify LLM-generated reasoning in radiation oncology.
By combining a novel step-and-shoot acquisition protocol with motion-compensated one-shot learning, S2V-DREME enables accurate time-resolved volumetric MRI reconstruction and motion tracking from cineslices, with strong potential for rapid volumetric imaging and real-time MR-guided adaptive radiotherapy.
Diffusion-weighted MRI (dMRI) is a powerful tool for quantifying cellular microenvironment parameters. This study proposes a physics-assisted deep learning (DL)-based denoising framework designed to enhance dMRI signal quality and improve the robustness of subsequent biophysical model fitting. A dataset of paired noise...
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