A multicenter pilot study evaluating simplified central vein assessment for the diagnosis of multiple sclerosis.

BACKGROUND: The central vein sign (CVS) is a proposed magnetic resonance imaging (MRI) biomarker for multiple sclerosis (MS); the optimal method for abbreviated CVS scoring is not yet established. OBJECTIVE: The aim of this study was to evaluate the performance of a simplified approach to CVS assessment in a multicenter study of patients being evaluated for suspected MS. METHODS: Adults referred for possible MS to 10 sites were recruited. A post-Gd 3D T2*-weighted MRI sequence (FLAIR*) was obtained in each subject. Trained raters at each site identified up to six CVS-positive lesions per FLAIR* scan. Diagnostic performance of CVS was evaluated for a diagnosis of MS which had been confirmed using the 2017 McDonald criteria at thresholds including three positive lesions (Select-3*) and six positive lesions (Select-6*). Inter-rater reliability assessments were performed. RESULTS: Overall, 78 participants were analyzed; 37 (47%) were diagnosed with MS, and 41 (53%) were not. The mean age of participants was 45 (range: 19-64) years, and most were female (n = 55, 71%). The area under the receiver operating characteristic curve (AUROC) for the simplified counting method was 0.83 (95% CI: 0.73-0.93). Select-3* and Select-6* had sensitivity of 81% and 65% and specificity of 68% and 98%, respectively. Inter-rater agreement was 78% for Select-3* and 83% for Select-6*. CONCLUSION: A simplified method for CVS assessment in patients referred for suspected MS demonstrated good diagnostic performance and inter-rater agreement.

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI.

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

Automated workflow for volumetric assessment of signal intensity ratio on T1-weighted MR images after multiple gadolinium administrations.

Purpose: Repeated injections of linear gadolinium-based contrast agent (GBCA) have shown correlations with increased signal intensities (SI) on unenhanced T1-weighted (T1w) images. Assessment is usually performed manually on a single slice and the SI as an average of a freehand region-of-interest is reported. We aim to develop a fully automated software that segments and computes SI ratio of dentate nucleus (DN) to pons (DN/P) and globus pallidus (GP) to thalamus (GP/T) for the assessment of gadolinium presence in the brain after a serial GBCA administrations. Approach: All patients ( N = 113 ) underwent at least eight GBCA enhanced scans. The modal SI in the DN, GP, pons, and thalamus were measured volumetrically on unenhanced T1w images and corrected based on the reference protocol (measurement 1) and compared to the SI-uncorrected-modal-volume (measurement 2), SI-corrected-mean-volume (measurement 3), as well as SI-corrected-modal-single slice (measurement 4) approaches. Results: Automatic processing worked on all 2119 studies (1150 at 1.5 T and 969 at 3 T). DN/P were 1.085 ± 0.048 (1.5 T) and 0.979 ± 0.061 (3 T). GP/T were 1.084 ± 0.039 (1.5 T) and 1.069 ± 0.042 (3 T). Modal DN/P ratios from volumetric assessment at 1.5 T failed to show a statistical difference with or without SI corrections ( p = 0.71 ). All other t -tests demonstrated significant differences (measurement 2, 3, 4 compared to 1, p < 0.001 ). Conclusion: The fully automatic method is an effective powerful tool to streamline the analysis of SI ratios in the deep brain tissues. Divergent SI ratios using different approaches reinforces the need to standardize the measurement for the research in this field.

Reproducibility of the Structural Connectome Reconstruction across Diffusion Methods.

Analysis of the structural connectomes can lead to powerful insights about the brain's organization and damage. However, the accuracy and reproducibility of constructing the structural connectome done with different acquisition and reconstruction techniques is not well defined. In this work, we evaluated the reproducibility of the structural connectome techniques by performing test-retest (same day) and longitudinal studies (after 1 month) as well as analyzing graph-based measures on the data acquired from 22 healthy volunteers (6 subjects were used for the longitudinal study). We compared connectivity matrices and tract reconstructions obtained with the most typical acquisition schemes used in clinical application: diffusion tensor imaging (DTI), high angular resolution diffusion imaging (HARDI), and diffusion spectrum imaging (DSI). We observed that all techniques showed high reproducibility in the test-retest analysis (correlation >.9). However, HARDI was the only technique with low variability (2%) in the longitudinal assessment (1-month interval). The intraclass coefficient analysis showed the highest reproducibility for the DTI connectome, however, with more sparse connections than HARDI and DSI. Qualitative (neuroanatomical) assessment of selected tracts confirmed the quantitative results showing that HARDI managed to detect most of the analyzed fiber groups and fanning fibers. In conclusion, we found that HARDI acquisition showed the most balanced trade-off between high reproducibility of the connectome, higher rate of path detection and of fanning fibers, and intermediate acquisition times (10-15 minutes), although at the cost of higher appearance of aberrant fibers.