OBJECTIVE. The objective of this study was to compare image quality and clinically
significant lesion detection on deep learning reconstruction (DLR) and iterative …

Purpose The purpose of this study was to compare the effect of two deep learning image
reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical …

Background Recently, computed tomography (CT) manufacturers have developed deep‐
learning‐based reconstruction algorithms to compensate for the limitations of iterative …

Background Ultra–low-dose (ULD) CT could facilitate the clinical implementation of large-
scale lung cancer screening while minimizing the radiation dose. However, traditional image …

Objective Iterative reconstruction degrades image quality. Thus, further advances in image
reconstruction are necessary to overcome some limitations of this technique in low-dose …

Objective To evaluate the effect of a commercial deep learning algorithm on the image
quality of chest CT, focusing on the upper abdomen. Methods One hundred consecutive …

Objectives We evaluated lower dose (LD) hepatic dynamic ultra-high-resolution computed
tomography (U-HRCT) images reconstructed with deep learning reconstruction (DLR) …

Objectives To assess the impact of a new artificial intelligence deep-learning reconstruction
(Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction …

Objective To compare the image quality between the vendor-agnostic and vendor-specific
algorithms on ultralow-dose chest CT. Methods Vendor-agnostic deep learning post …

Purpose To compare the impact on CT image quality and dose reduction of two versions of a
Deep Learning Image Reconstruction algorithm. Material and methods Acquisitions on the …