ABSTRACT A new large-scale computational total variation regularization algorithm is
introduced and tested with examples arising from X-ray tomography with sparsely sampled …

This paper experimentally demonstrates a feasibility of moving beam-blocker-based low-
dose cone-beam CT (CBCT) and exploits the beam-blocking configurations to reach an …

Abstract Background The sparse CT (Computed Tomography), inspired by compressed
sensing, means to introduce a prior information of image sparsity into CT reconstruction to …

Recent advances in total variation (TV) technology enable accurate CT image reconstruction
from highly under-sampled and noisy projection data. The standard iterative reconstruction …

This work is to develop a general framework, namely analytical iterative reconstruction (AIR)
method, to incorporate analytical reconstruction (AR) method into iterative reconstruction …

Recently, we proposed a new algorithm of accelerated barrier optimization compressed
sensing (ABOCS) for iterative CT reconstruction. The previous implementation of ABOCS …

Low-dose computed tomography (LDCT) is desirable for both diagnostic imaging and image-
guided interventions. Denoisers are widely used to improve the quality of LDCT. Deep …

Photon counting detector (PCD)-based spectral computed tomography (CT) is a promising
imaging technique that enables high energy resolution imaging with narrow energy bins …

Objectives Adaptive steepest descent projection onto convex sets (ASD-POCS) algorithms
with Lp-norm (0< p≤ 1) regularization have shown great promise in sparse-view X-ray CT …