Schilling, Kurt G.

The spinal cord plays a central role in the pathophysiology and clinical manifestations of multiple sclerosis (MS), yet remains under-studied compared with the brain. This review summarizes key insights from the 2025 North American Imaging in MS Spinal Cord Imaging Workshop, highlighting recent advances, ongoing challenges, and future opportunities in MS spinal cord imaging. We review pathological studies and outline the clinical relevance of spinal cord lesions and atrophy for diagnosis, prognosis, and disease monitoring, highlighting emerging biomarkers of progression independent of relapse activity. Correlations between magnetic resonance imaging, histopathology, and clinical outcomes support the validation and translational potential of advanced spinal cord imaging techniques. Finally, we discuss spinal cord–specific processing pipelines and reproducibility challenges. Collectively, these insights underscore the need to integrate advanced and quantitative spinal cord imaging into clinical trials, research studies, and—when feasible—clinical care, to fully capture the extent of MS pathology, and ultimately improve patient outcomes.

Background/Objectives: White matter (WM) tract detection is critical in the presurgical planning of tumor resection. However, standard-of-care imaging techniques including T1-weighted, T2-weighted, and Diffusion Tensor Imaging (DTI) often fail to identify WM tracts within edematous regions. In T1/T2-weighted imaging, edema increases extracellular water and reduces tissue contrast, and in diffusion-weighted imaging, edema elevates isotropic diffusion, reducing sensitivity to anisotropic diffusion along WM tracts. Advanced biophysical diffusion modeling techniques such as Neurite Orientation Dispersion and Density Imaging (NODDI) and the Standard Model (SM) address this limitation by compartmentalizing the diffusion signal into free-water, intra-neurite, and extra-neurite contributions. Here, we test if biophysical multi-compartment models can robustly identify WM tracts and recover tractography streamlines within edematous regions. Methods: In this study, we use multi-shell diffusion-weighted MRI data obtained from patients with meningiomas—a pathology allowing for isolation of the effects of edema without the confounding effects of tumor cell invasion. We compared FA from standard and free-water-corrected DTI, the orientation dispersion index (ODI) from NODDI, and P2 (a scalar descriptor of fiber orientation coherence) from the SM fODF in edematous and unaffected contralateral WM regions. As a proof of concept, we visually evaluated the tractography performance across models. Results: Our results show that (1 − ODI) and P2 values in edema remained close to within-subject contralateral measurements, contrasting with substantial reductions in FA and FW-FA. (1 − ODI) showed a small but statistically significant increase in edema (~8%, p = 0.02), while P2 was unchanged. Conclusions: These results highlight the potential of biophysical diffusion models for preoperative mapping in edema.

Purpose Diffusion‐weighted imaging allows investigators to identify structural, microstructural, and connectivity‐based differences between subjects, but variability due to session and scanner biases is a challenge. Methods To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm 2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de‐identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi‐compartment neurite orientation dispersion and density model, (3) white‐matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region‐wise fractional anisotropy, mean diffusivity, and principal eigenvector; region‐wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle‐wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. Results We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. Conclusions This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.