Deep Learning Reconstruction in Abdominopelvic Contrast-Enhanced CT for The Evaluation of Hemorrhages.

Purpose: To investigate the effects of deep learning reconstruction on depicting arteries and providing suitable images for the evaluation of hemorrhages with abdominopelvic contrast-enhanced computed tomography (CT) compared with hybrid iterative reconstruction.

Methods: This retrospective study included 16 patients (mean age: 54.2 ± 22.

Effect of Deep Learning Reconstruction on Evaluating Cervical Spinal Canal Stenosis With Computed Tomography.

Objective: Magnetic resonance imaging (MRI) is commonly used to evaluate cervical spinal canal stenosis; however, some patients are ineligible for MRI. We aimed to assess the effect of deep learning reconstruction (DLR) in evaluating cervical spinal canal stenosis using computed tomography (CT) compared with hybrid iterative reconstruction (hybrid IR).

Methods: This retrospective study included 33 patients (16 male patients; mean age, 57.

Deep Learning Reconstruction Plus Single-Energy Metal Artifact Reduction for Supra Hyoid Neck CT in Patients With Dental Metals.

We investigated the effect of deep learning reconstruction (DLR) plus single-energy metal artifact reduction (SEMAR) on neck CT in patients with dental metals, comparing it with DLR and with hybrid iterative reconstruction (Hybrid IR)-SEMAR. In this retrospective study, 32 patients (25 men, 7 women; mean age: 63 ± 15 years) with dental metals underwent contrast-enhanced CT of the oral and oropharyngeal regions. Axial images were reconstructed using DLR, Hybrid IR-SEMAR, and DLR-SEMAR.

Assessing the Effects of Deep Learning Reconstruction on Abdominal CT Without Arm Elevation.

To evaluate the effects of deep learning reconstruction (DLR) on image quality of abdominal computed tomography (CT) in patients without arm elevation compared with hybrid-iterative reconstruction (Hybrid-IR) and filtered back projection (FBP). In this retrospective study, axial images of 26 patients who underwent CT without arm elevation were reconstructed using DLR, Hybrid-IR, and FBP. Streak artifact index (SAI) was calculated by dividing the standard deviation of CT attenuation in the liver or spleen by that in fat.

Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction.

Purpose: To compare image quality and interobserver agreement in evaluations of neuroforaminal stenosis between 1.5T cervical spine magnetic resonance imaging (MRI) with deep learning reconstruction (DLR) and 3T MRI without DLR.

Methods: In this prospective study, 21 volunteers (mean age: 42.

Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes.

Objectives: To investigate whether deep learning reconstruction (DLR) provides improved cervical spine MR images using a 1.5 T unit in the evaluation of degenerative changes without increasing imaging time.

Methods: This study included 21 volunteers (age 42.