… under which the TOF MR angiography was performed. … Deep learning is a subfield of
machine learning pertaining to … , we used deep learning techniques with MR angiography …

… for magnetic resonance (MR) in patients with congenital heart disease (CHD). Contrast-enhanced
MR angiography (CE-… It has been shown that a deep learning postprocessing step can …

… To develop a deep learning algorithm for automated detection and localization of intracranial
aneurysms on time-of-flight MR angiography and evaluate its diagnostic performance. …

Purpose To develop and evaluate a novel and generalizable super‐resolution (SR) deep‐learning
framework for motion‐compensated isotropic 3D coronary MR angiography (CMRA), …

… The purpose of this study was to evaluate whether deep learning reconstruction (DLR)
improves the image quality of intracranial magnetic resonance angiography (MRA) at 1.5 T. …

… Schema of super-resolution deep learning-based reconstruction using k-space properties.
The first schema (a) illustrates the entire process of the super-resolution deep learning-based …

… We evaluate the feasibility of using deep learning to automatically detect intracranial
arterial steno-occlusive lesions from time-of-flight magnetic resonance angiography. …

… In this paper, we propose a deep learning-based 3D volume segmentation framework to
address the above limitations. The proposed method is an end-to-end segmentation network …

Background The clinical application of coronary MR angiography (MRA) remains limited due
to its long acquisition time and often unsatisfactory image quality. A compressed sensing …

… In this study, we presented a deep learning algorithm for stenosis detection via the 3D SE-…
As a result, our model outperformed recent 3D deep learning VGG and ResNet approaches …