The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as
skull-stripping, is an integral component of many neuroimage analysis streams. Despite their …

Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has
shown promise in imaging of moving subjects, eg, fetal MRI. However, existing slice-to …

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders
affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway …

Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to
alleviate the burden on radiologists and MR technologists, and improve throughput. The …

Affine image registration is a cornerstone of medical-image analysis. While classical
algorithms can achieve excellent accuracy, they solve a time-consuming optimization for …

Synthetic data have emerged as an attractive option for developing machine-learning
methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)—a …

Fetal ultrasonography (US) and MR imaging provide essential information in the evaluation
and management of pregnancies and have been shown to improve perinatal outcomes in …

Purpose Widening the availability of fetal MRI with fully automatic real‐time planning of
radiological brain planes on 0.55 T MRI. Methods Deep learning‐based detection of key …

Automated fetal brain extraction from full-uterus MRI is a challenging task due to variable
head sizes, orientations, complex anatomy, and prevalent artifacts. While deep-learning …

Purpose: Widening the availability of fetal MRI with fully automatic real-time planning of
radiological brain planes on 0.55 T MRI. Methods: Deep learning-based detection of key …