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 accelerating complexity and variety of medical imaging devices and methods have
outpaced the ability to evaluate and optimize their design and clinical use. This is a …

The combination of tomographic imaging and deep learning, or machine learning in
general, promises to empower not only image analysis but also image reconstruction. The …

There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality
and increase applicability of x-ray CT imaging. IR has the ability to significantly reduce …

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 …

Advanced manufacturing technologies, led by additive manufacturing, have undergone
significant growth in recent years. These technologies enable engineers to design parts with …

Artifacts resulting from metal objects have been a persistent problem in CT images over the
last four decades. A common approach to overcome their effects is to replace corrupt …

Objective. X-ray-based imaging modalities including mammography and computed
tomography (CT) are widely used in cancer screening, diagnosis, staging, treatment …

Tomographic/tomosynthetic image reconstruction systems and methods in the framework of
machine learning, such as deep learning, are provided. A machine learning algorithm can …

Abstract Machine learning and deep learning are rapidly finding applications in the medical
imaging field. In this paper, we address the long-standing problem of metal artifacts in …