K. Joost Batenburg

The authors propose FMCT, the first flow-matching-based CT reconstruction framework, and an efficient variant EFMCT that reuses previously predicted velocity fields over consecutive steps to cut neural network function evaluations (NFEs), motivated by the deterministic ODE sampling of flow matching being naturally compatible with repeated data consistency corrections and by the strong correlation of predicted velocities across adjacent steps; they provide a theoretical analysis showing the reuse error is of the same order as Euler discretization and bounded for a bounded number of consecutive reuse steps, and on the AAPM and Medical Segmentation Decathlon CT datasets with 20-view and 40-view parallel-beam sparse sampling, FMCT/EFMCT achieve PSNR/SSIM competitive with diffusion-based method

The authors propose FMCT, the first flow-matching-based CT reconstruction framework, and an efficient variant EFMCT that reuses previously predicted velocity fields over consecutive steps to cut neural network function evaluations (NFEs), motivated by the deterministic ODE sampling of flow matching being naturally compatible with repeated data consistency corrections and by the strong correlation of predicted velocities across adjacent steps; they provide a theoretical analysis showing the reuse error is of the same order as Euler discretization and bounded for a bounded number of consecutive reuse steps, and on the AAPM and Medical Segmentation Decathlon CT datasets with 20-view and 40-view parallel-beam sparse sampling, FMCT/EFMCT achieve PSNR/SSIM competitive with diffusion-based method