LST-AI: A deep learning ensemble for accurate MS lesion segmentation.

Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt.

Quantitative susceptibility mapping in multiple sclerosis: A systematic review and meta-analysis.

Background: Quantitative susceptibility mapping (QSM) is a quantitative measure based on magnetic resonance imaging sensitive to iron and myelin content. This makes QSM a promising non-invasive tool for multiple sclerosis (MS) in research and clinical practice.

Objective: We performed a systematic review and meta-analysis on the use of QSM in MS.

Intensity scaling of conventional brain magnetic resonance images avoiding cerebral reference regions: A systematic review.

Background: Conventional brain magnetic resonance imaging (MRI) produces image intensities that have an arbitrary scale, hampering quantification. Intensity scaling aims to overcome this shortfall. As neurodegenerative and inflammatory disorders may affect all brain compartments, reference regions within the brain may be misleading.

Cortical Thin Patch Fraction Reflects Disease Burden in MS: The Mosaic Approach.

Background And Purpose: GM pathology plays an essential role in MS disability progression, emphasizing the importance of neuroradiologic biomarkers to capture the heterogeneity of cortical disease burden. This study aimed to assess the validity of a patch-wise, individual interpretation of cortical thickness data to identify GM pathology, the "mosaic approach," which was previously suggested as a biomarker for assessing and localizing atrophy.

Materials And Methods: We investigated the mosaic approach in a cohort of 501 patients with MS with respect to 89 internal and 651 external controls.