Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have
shown great potential in enhancing diagnosis and prognosis prediction in patients with …

Patient motion impacts single photon emission computed tomography (SPECT), positron
emission tomography (PET) and x-ray computed tomography (CT) by giving rise to …

Deep learning (DL) algorithms have recently emerged from machine learning and soft
computing techniques. Since then, several deep learning (DL) algorithms have been …

Artificial intelligence (AI) refers to the use of computational techniques to mimic human
thought processes and learning capacity. The past decade has seen a rapid proliferation of …

Purpose To rapidly reconstruct undersampled 3D non‐Cartesian image‐based navigators
(iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in …

Motion artifacts are a major factor that can degrade the diagnostic performance of computed
tomography (CT) images. In particular, the motion artifacts become considerably more …

Purpose During a typical cardiac short scan, the heart can move several millimeters. As a
result, the corresponding CT reconstructions may be corrupted by motion artifacts …

Purpose. Patient motion artifacts present a prevalent challenge to image quality in
interventional cone-beam CT (CBCT). We propose a novel reference-free similarity metric …

Background Patient motions are a repeatedly reported phenomenon in oral and
maxillofacial cone beam CT scans, leading to reconstructions of limited usability. In certain …

Objectives Coronary motion artifacts affect the diagnostic accuracy of coronary CT
angiography (CCTA), especially in the mid right coronary artery (mRCA). The purpose is to …