Statistical X-ray computed tomography (CT) reconstruction can improve image quality from
reduced dose scans, but requires very long computation time. Ordered subsets (OS) …

Background and objective X-ray computed tomography (CT) plays an important role in
modern medical science. Human health problems caused by CT radiation have attracted the …

This work addresses the resolution of penalized least-squares problems using the proximal
gradient algorithm (PGA). PGA can be accelerated by preconditioning strategies. However …

The presence of high-density objects remains an open problem in medical CT imaging. Data
of projections passing through objects of high density, such as metal implants, are …

Image denoising is a fundamental operation in image processing, and its applications range
from the direct (photographic enhancement) to the technical (as a subproblem in image …

Preconditioners, especially diagonal ones, have been key ingredients in several state-of-the-
art optimization algorithms for transmission and emission tomographic image reconstruction …

Model-based iterative reconstruction (MBIR) methods based on maximum a posteriori (MAP)
estimation have been recently introduced to multi-slice CT scanners. The model-based …

We present group coordinate descent algorithms for edge-preserving image denoising that
are particularly well-suited to the graphics processing unit (GPU). The algorithms decouple …

Model-based image reconstruction (MBIR) is a powerful technique for solving ill-posed
inverse problems. Compared with direct methods, it can provide better estimates from noisy …

Splitting-based CT reconstruction algorithms decompose the reconstruction problem into a
iterated sequence of “easier” subproblems. One relatively memory-efficient algorithm …