In 1971, the first patient CT examination by Ambrose and Hounsfield paved the way for not
only volumetric imaging of the brain but of the entire body. From the initial 5-minute scan for …

CT reconstruction has undergone a substantial change over the last decade with the
introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR) …

Background The use of computed tomography (CT) for attenuation correction (AC) in whole‐
body PET/CT can result in a significant contribution to radiation exposure. This can become …

(1) This study evaluates the impact of an AI denoising algorithm on image quality, diagnostic
accuracy, and radiological workflows in pediatric chest ultra-low-dose CT (ULDCT).(2) …

The implementation of deep neural networks has spurred the creation of deep learning
reconstruction (DLR) CT algorithms. DLR CT techniques encompass a spectrum of deep …

Rationale and Objectives Traumatic neuroradiological emergencies necessitate rapid and
accurate diagnosis, often relying on computed tomography (CT). However, the associated …

Deep-learning (DL) noise reduction techniques in computed tomography (CT) are expected
to reduce the image noise while maintaining the clinically relevant information in reduced …

Objective. Reducing CT radiation dose is an often proposed measure to enhance patient
safety, which, however results in increased image noise, translating into degradation of …

Non-contrast computed tomography (CT) is commonly used for the evaluation of various
pathologies including pulmonary infections or urolithiasis but, especially in low-dose …

Discussion As depicted in the main table, the majority of FDA-approved AI products are
developed by companies headquartered in Europe and the North America. Another …