Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …

This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

The first CT scanners in the early 1970s already used iterative reconstruction algorithms;
however, lack of computational power prevented their clinical use. In fact, it took until 2009 …

The promise of compressive sensing, exploitation of compressibility to achieve high quality
image reconstructions with less data, has attracted a great deal of attention in the medical …

Low‐dose X‐ray computed tomography (LDCT) imaging is highly recommended for use in
the clinic because of growing concerns over excessive radiation exposure. However, the CT …

Tomographic image reconstruction is a computationally demanding task, even more so
when advanced models are used to describe a more complete and accurate picture of the …

Photon-counting computed tomography (PCCT) is an emerging imaging technique that
enables multi-energy imaging with only a single scan acquisition. To enable multi-energy …

Robotic C-arms are capable of complex orbits that can increase field of view, reduce
artifacts, improve image quality, and/or reduce dose; however, it can be challenging to …

Statistical image reconstruction (SIR) methods have shown potential to substantially improve
the image quality of low‐dose x‐ray computed tomography (CT) as compared to the …

Markov random field (MRF) model has been widely employed in edge-preserving regional
noise smoothing penalty to reconstruct piece-wise smooth images in the presence of noise …