Summary The 2015 Magnetic Resonance Imaging in Multiple Sclerosis and 2016
Consortium of Multiple Sclerosis Centres guidelines on the use of MRI in diagnosis and …

The current diagnostic criteria for multiple sclerosis (MS) lack specificity, and this may lead to
misdiagnosis, which remains an issue in present-day clinical practice. In addition …

The performance of current machine learning methods to detect heterogeneous pathology is
limited by the quantity and quality of pathology in medical images. A possible solution is …

Magnetic resonance imaging (MRI) is a fundamental tool in the diagnosis and management
of neurological diseases such as multiple sclerosis (MS). New portable, low-field strength …

Expert interpretation of anatomical images of the human brain is the central part of
neuroradiology. Several machine learning-based techniques have been proposed to assist …

Abstract Analysis of 3D medical imaging data has been a large topic of focus in the area of
Machine Learning/Artificial Intelligence, though little work has been done in algorithmic …

Background: Performing routine brain magnetic resonance imaging (MRI) is widely
accepted as the standard of care for disease monitoring in multiple sclerosis (MS), but the …

Objective In the work-up of patients presenting with a clinically isolated syndrome (CIS), 3T
MRI might offer a higher lesion detection than 1.5 T, but it remains unclear whether this …

There is an increasing need of sharing harmonized data from large, cooperative studies as
this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple …

BACKGROUND AND PURPOSE: MR imaging rescans and recalls can create large hospital
revenue loss. The purpose of this study was to develop a fast, automated method for …