# CT Reconstruction

The authors introduce TomoTransformer, a transformer architecture that treats each local filtered projection as an individual token and predicts missing views via self-attention in a back-projection space, yielding a single foundation model that can process any number of input projections at arbitrary angular locations and detector dimensions and query any number of target angles without retraining; trained on a large-scale dataset spanning diverse medical CT anatomies and natural images, it significantly outperforms concurrent multi-purpose models like ViewTrans and matches or exceeds strong protocol-specific baselines on several sparse-view benchmarks, while showing robust zero-shot generalization on real experimental nanoscale brain data collected from an X-ray synchrotron.

The authors introduce TomoTransformer, a transformer architecture that treats each local filtered projection as an individual token and predicts missing views via self-attention in a back-projection space, yielding a single foundation model that can process any number of input projections at arbitrary angular locations and detector dimensions and query any number of target angles without retraining; trained on a large-scale dataset spanning diverse medical CT anatomies and natural images, it significantly outperforms concurrent multi-purpose models like ViewTrans and matches or exceeds strong protocol-specific baselines on several sparse-view benchmarks, while showing robust zero-shot generalization on real experimental nanoscale brain data collected from an X-ray synchrotron.

The work proposes a zero-shot 3D CT super-resolution framework: it first trains a diffusion model on abundant 2D X-ray data and uses DDNM/DDNM+ to upsample low-resolution CT projections into high-resolution projection priors, then applies a new Negative Alpha Blending Gaussian Splatting (NAB-GS) that models positive and negative Gaussian densities to learn the signed residual between diffusion-generated HR projections and upsampled LR projections for HR volume reconstruction; on the two public datasets UHRCT and MELA it achieves higher PSNR and SSIM than trilinear, cubic, NeRF, and CuNeRF zero-shot methods, is competitive with the supervised ArSSR, runs in about 15 minutes per volume, and two domain experts judged the 4× results to have clinical potential while 8× still needs improvement.

The work proposes a zero-shot 3D CT super-resolution framework: it first trains a diffusion model on abundant 2D X-ray data and uses DDNM/DDNM+ to upsample low-resolution CT projections into high-resolution projection priors, then applies a new Negative Alpha Blending Gaussian Splatting (NAB-GS) that models positive and negative Gaussian densities to learn the signed residual between diffusion-generated HR projections and upsampled LR projections for HR volume reconstruction; on the two public datasets UHRCT and MELA it achieves higher PSNR and SSIM than trilinear, cubic, NeRF, and CuNeRF zero-shot methods, is competitive with the supervised ArSSR, runs in about 15 minutes per volume, and two domain experts judged the 4× results to have clinical potential while 8× still needs improvement.

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