MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction

arXiv:2405.05814v1 Announce Type: cross
Abstract: Computed Tomography (CT) technology reduces radiation haz-ards to the human body through sparse sampling, but fewer sampling angles pose challenges for image reconstruction. Score-based generative models are widely used in sparse-view CT re-construction, performance diminishes significantly with a sharp reduction in projection angles. Therefore, we propose an ultra-sparse view CT reconstruction method utilizing multi-scale dif-fusion models (MSDiff), designed to concentrate on the global distribution of information and facilitate the reconstruction of sparse views with local …

abstract arxiv challenges construction cs.cv diffusion diffusion model eess.iv generative generative models human image performance projection sampling scale technology through type view