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The Washington University and Barnes-Jewish Hospital Transplant Center has one of the oldest and most established transplant programs in the country.

Objective: To enable use of clinical magnetic resonance images (MRIs) to quantify abnormalities in normal appearing (NA) white matter (WM) and gray matter (GM) in multiple sclerosis (MS) and to determine associations with MS-related disability. Identification of these abnormalities heretofore has required specialized scans not routinely available in clinical practice. 

A 35-year-old man with an enhancing tumefactive brain lesion underwent biopsy, revealing inflammatory demyelination. We used quantitative Gradient- Recalled-Echo (qGRE) MRI to visualize and measure tissue damage in the lesion. Two weeks after biopsy, qGRE showed significant R2t* reduction in the left optic radiation and surrounding tissue, consistent with the histopathological and clinical findings. qGRE was repeated 6 and 14 months later, demonstrating partially recovered optic radiation R2t*, in concert with improvement of the hemianopia to ultimately involve only the lower right visual quadrant. These results support qGRE metrics as in vivo biomarkers for tissue damage and longitudinal monitoring of demyelinating disease.

Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods.

Myelin oligodendrocyte glycoprotein antibody disease (MOGAD) can radiographically mimic multiple sclerosis (MS) and aquaporin-4 (AQP4) antibody-positive neuromyelitis optica spectrum disorder (NMOSD). Central vein sign (CVS) prevalence has not yet been well-established in MOGAD. The objective of this study is to characterize the magnetic resonance imaging (MRI) appearance and CVS prevalence of MOGAD patients in comparison to matched cohorts of MS and AQP4+ NMOSD.