Person:

Arora, A

Purpose We tested the validity of a freely available segmentation pipeline to measure compartmental brain volumes from 3T MRI in patients with multiple sclerosis (MS). Our primary focus was methodological to explore the effect of segmentation corrections on the clinical relevance of the output metrics. Methods: Three-dimensional T1-weighted images were acquired to compare 61 MS patients to 30 age- and gender-matched normal controls (NC). We also tested the within patient MRI relationship to disability (eg, expanded disability status scale [EDSS] score) and cognition. Statistical parametric mapping v. 8 (SPM8)-derived gray matter (GMF), white matter (WMF), and total brain parenchyma fractions (BPF) were derived before and after correcting errors from T1 hypointense MS lesions and/or ineffective deep GM contouring. Results: MS patients had lower GMF and BPF as compared to NC (P<.05). Cognitively impaired patients had lower BPF than cognitively preserved patients (P<.05). BPF was related to EDSS; BPF and GMF were related to disease duration (all P<.05). Errors caused bias in GMFs and WMFs but had no discernable influence on BPFs or any MRI-clinical associations. Conclusions: We report the validity of a segmentation pipeline for the detection of MS-related brain atrophy with 3T MRI. Longitudinal studies are warranted to extend these results.

Background/Purpose: To assess correlation between brain lesions and clinical status with 1.5T and 3T magnetic resonance imaging (MRI). Methods: Brain MRI fluid-attenuated inversion-recovery (FLAIR) sequences were performed in 32 multiple sclerosis (MS) patients. Expanded Disability Status Scale (EDSS) score (mean ± standard deviation) was 2 ± 2.0 (range 0-8), disease duration 9.3 ± 8.0 (range .8-29) years. Results: FLAIR lesion volume (FLLV) at 3T was higher than at 1.5T (P= .01). Correlation between 1.5T FLLV and EDSS score was poor, while 3T FLLV correlated moderately and significantly (rs= .39, P= .03). When controlling for age and depression, correlations between FLLV and cognitive measures were significant at 1.5T for the Judgment of Line Orientation test (JLO) (rs=−.44, P= .05), the Symbol Digit Modalities Test (SDMT) (rs=−.49, P= .02), and the California Verbal Learning Test Delayed Free Recall (CVLT DR) (rs=−.44, P= .04). Correlations at 3T were also significant for these tests, but of greater magnitude: JLO (rs=−.70, P= .0005), SDMT (rs=−.73, P= .0001), CVLT DR (rs=−.061, P= .003). Additional significant correlations obtained only at 3T included the 2 second-paced auditory serial addition test (rs=−.55, P= .01), the Brief Visuospatial Memory Test-Delayed Free Recall (rs=−.56, P= .007), and the California Verbal Learning Test Total Recall (rs=−.42, P= .05). Conclusion: MRI at 3T may boost sensitivity and improve validity in MS brain lesion assessment.

Background and Purpose: VBM has been widely used to study GM atrophy in MS. MS lesions lead to segmentation and registration errors that may affect the reliability of VBM results. Improved segmentation and registration have been demonstrated by WM LI before segmentation. DARTEL appears to improve registration versus the USM. Our aim was to compare the performance of VBM-DARTEL versus VBM-USM and the effect of LI in the regional analysis of GM atrophy in MS. Materials and Methods: 3T T1 MR imaging scans were acquired from 26 patients with RRMS and 28 age-matched NC. LI replaced WM lesions with normal-appearing WM intensities before image segmentation. VBM analysis was performed in SPM8 by using DARTEL and USM with and without LI, allowing the comparison of 4 VBM methods (DARTEL + LI, DARTEL − LI, USM + LI, and USM − LI). Accuracy of VBM was assessed by using NMI, CC, and a simulation analysis. Results: Overall, DARTEL + LI yielded the most accurate GM maps among the 4 methods (highest NMI and CC, P < .001). DARTEL + LI showed significant GM loss in the bilateral thalami and caudate nuclei in patients with RRMS versus NC. The other 3 methods overestimated the number of regions of GM loss in RRMS versus NC. LI improved the accuracy of both VBM methods. Simulated data suggested the accuracy of the results provided from patient MR imaging analysis. Conclusions: We introduce a pipeline that shows promise in limiting segmentation and registration errors in VBM analysis in MS.