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 …

This paper introduces the capabilities and availability of a customizable scientific software
package called Livermore Tomography Tools (LTT) built for computed tomography (CT) …

Purpose Recently, several attempts were conducted to transfer deep learning to medical
image reconstruction. An increasingly number of publications follow the concept of …

Purpose: To allow for a purely image‐based motion estimation and compensation in weight‐
bearing cone‐beam computed tomography of the knee joint. Methods: Weight‐bearing …

In this paper, we consider the use of prior knowledge within neural networks. In particular,
we investigate the effect of a known transform within the mapping from input data space to …

Deep learning-based image processing is capable of creating highly appealing results.
However, it is still widely considered as a “blackbox” transformation. In medical imaging, this …

Purpose: AC‐arm CT system has been shown to be capable of scanning a single cadaver
leg under loaded conditions by virtue of its highly flexible acquisition trajectories. In Part I of …

This paper addresses streak reduction in limited angle tomography. Although the iterative
reweighted total variation (wTV) algorithm reduces small streaks well, it is rather inept at …

Radiation exposure in CT imaging leads to increased patient risk. This motivates the pursuit
of reduced-dose scanning protocols, in which noise reduction processing is indispensable …

Purpose Benefiting from multi‐energy x‐ray imaging technology, material decomposition
facilitates the characterization of different materials in x‐ray imaging. However, the …