Objective: To investigate the performance of deep learning (DL) based on fully convolutional
neural network (FCNN) in segmenting brain tissues in a large cohort of multiple sclerosis (…

Background Enhancing lesions on MRI scans obtained after contrast material administration
are commonly thought to represent disease activity in multiple sclerosis (MS); it is desirable …

Background Deep learning (DL) is a promising methodology for automatic detection of
abnormalities in brain MRI. Purpose To automatically evaluate the quality of multicenter structural …

Background The dependence of deep‐learning (DL)‐based segmentation accuracy of brain
MRI on the training size is not known. Purpose To determine the required training size for a …

Purpose: Ongoing post-stroke structural degeneration and neuronal loss preceding
neuropsychological symptoms such as cognitive decline and depression are poorly understood. …

Background It has been hypothesized that the supply of chemical energy may be insufficient
to fuel normal mechanical pump function in heart failure (HF). The creatine kinase (CK) …

Objective: The aim of this study is to assess the performance of deep learning convolutional
neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort …

Speed and signal-to-noise ratio (SNR) are critical for localized magnetic resonance spectroscopy
(MRS) of low-concentration metabolites. Matching voxels to anatomical compartments …

Functional assessment of the dural lymphatic vessels using dynamic contrast MRI in multiple
sclerosis - Gabr - 2023 - Brain and … Refaat Gabr is now an employee of Biogen Inc. …

Destructive interference from phase fluctuations caused by motion during 1 H magnetic
resonance spectroscopy (MRS) stimulated‐echo acquisition mode (STEAM) and point‐resolved …