Purpose The purpose of this study was to evaluate whether deep learning reconstruction
(DLR) improves the image quality of intracranial magnetic resonance angiography (MRA) at …

Purpose The aim of this study is to assess the effect of super-resolution deep learning-based
reconstruction (SR-DLR), which uses k-space properties, on image quality of intracranial …

Objectives To develop a generative adversarial network (GAN) model to improve image
resolution of brain time-of-flight MR angiography (TOF-MRA) and to evaluate the image …

Objectives Quantitative synthetic magnetic resonance imaging (MRI) enables synthesis of
various contrast-weighted images as well as simultaneous quantification of T1 and T2 …

To evaluate the ability of a commercialized deep learning reconstruction technique to depict
intracranial vessels on the brain computed tomography angiography and compare the …

Introduction Deep learning–based MRI reconstruction has recently been introduced to
improve image quality. This study aimed to evaluate the performance of deep learning …

Purpose To develop and evaluate a method of parallel imaging time‐of‐flight (TOF) MRA
using deep multistream convolutional neural networks (CNNs). Methods A deep parallel …

Background Computed tomography angiography (CTA) is very popular because it is
characterized by rapidity and accessibility. However, CTA is inferior to digital subtraction …

Background and objectives Intracranial atherosclerotic stenosis of a major intracranial artery
is the common cause of ischemic stroke. We evaluate the feasibility of using deep learning …

Objectives To evaluate image quality and reconstruction times of a commercial deep
learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid …