RA-CMF integrates conditional MeanFlow for trajectory-based image enhancement with an RL-driven policy for tile-wise adaptive refinement budgets, achieving average PSNR of 34.23 and SSIM of 0.95 on CT images with strong tumor ROI radiomic feature consistency.
Deep learning–based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses
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RA-CMF: Region-Adaptive Conditional MeanFlow for CT Image Reconstruction
RA-CMF integrates conditional MeanFlow for trajectory-based image enhancement with an RL-driven policy for tile-wise adaptive refinement budgets, achieving average PSNR of 34.23 and SSIM of 0.95 on CT images with strong tumor ROI radiomic feature consistency.