Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

The theory of task-based assessment of image quality is reviewed in the context of imaging
with ionizing radiation, and objective figures of merit (FOMs) for image quality are …

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 …

The purpose of this study was to develop a CT simulation platform that is: 1) compatible with
voxel-based computational phantoms; 2) capable of modeling the geometry and physics of …

We explore the use of the recently proposed'total nuclear variation'(TV N) as a regularizer for
reconstructing multi-channel, spectral CT images. This convex penalty is a natural extension …

CT is the frontline imaging modality for diagnosis of acute traumatic brain injury (TBI),
involving the detection of fresh blood in the brain (contrast of 30–50 HU, detail size down to …

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 …

Purpose: Nonstationarity is an important aspect of imaging performance in CT and cone‐
beam CT (CBCT), especially for systems employing iterative reconstruction. This work …

The area of machine learning, especially deep learning, has exploded in recent years,
producing advances in everything from speech recognition and gaming to drug discovery …

Compared to conventional computed tomography (CT), spectral CT can provide the
capability of material decomposition, which can be used in many clinical diagnosis …