Iterative reconstruction (IR) methods have recently re-emerged in transmission x-ray
computed tomography (CT). They were successfully used in the early years of CT, but given …

Image processing plays a crucial role in maximising diagnostic quality of positron emission
tomography (PET) images. Recently, deep learning methods developed across many fields …

In this paper, we present a semi-supervised deep learning approach to accurately recover
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …

Purpose To describe a large, publicly available dataset comprising computed tomography
(CT) projection data from patient exams, both at routine clinical doses and simulated lower …

Coordinate descent algorithms solve optimization problems by successively performing
approximate minimization along coordinate directions or coordinate hyperplanes. They have …

Model-based reconstruction is a powerful framework for solving a variety of inverse
problems in imaging. In recent years, enormous progress has been made in the problem of …

Non-negative matrix factorization (NMF) has previously been shown to be a useful
decomposition for multivariate data. Two different multi-plicative algorithms for NMF are …

The development of energy selective, photon counting X-ray detectors allows for a wide
range of new possibilities in the area of computed tomographic image formation. Under the …

The expectation-maximization (EM) method can facilitate maximizing likelihood functions
that arise in statistical estimation problems. In the classical EM paradigm, one iteratively …

Multislice helical computed tomography scanning offers the advantages of faster acquisition
and wide organ coverage for routine clinical diagnostic purposes. However, image …