Public articles linked to the same research event.
arXiv 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 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.