Score-based generative model (SGM) has risen to prominence in sparse-view CT
reconstruction due to its impressive generation capability. The consistency of data is crucial …

Better algorithms for medical image reconstruction can improve image quality and enable
reductions in acquisition time and radiation dose. A prior understanding of the distribution of …

The multi-source stationary CT, where both the detector and X-ray source are fixed,
represents a novel imaging system with high temporal resolution that has garnered …

The score-based generative model (SGM) has received significant attention in the field of
medical imaging, particularly in the context of limited-angle computed tomography (LACT) …

Score-based generative model (SGM) has demonstrated great potential in the challenging
limited-angle CT (LA-CT) reconstruction. SGM essentially models the probability density of …

Deep generative modeling has emerged as a powerful tool for synthesizing realistic medical
images, driving advances in medical image analysis, disease diagnosis, and treatment …

Score-based generative models (SGMs) have gained prominence in sparse-view CT
reconstruction for their precise sampling of complex distributions. In SGM-based …

Abstract Background and Objective We develop an efficient deep-learning based dual-
domain reconstruction method for sparse-view CT reconstruction with small training …

Objective. Computed Tomography (CT) has been widely used in industrial high-resolution
non-destructive testing. However, it is difficult to obtain high-resolution images for large …

Metal artifact reduction (MAR) is important for clinical diagnosis with CT images. The existing
state-of-the-art deep learning methods usually suppress metal artifacts in sinogram or image …