RT Journal Article
SR Electronic
T1 The Impact of Lesion In-Painting and Registration Methods on Voxel-Based Morphometry in Detecting Regional Cerebral Gray Matter Atrophy in Multiple Sclerosis
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
DO 10.3174/ajnr.A3083
A1 A. Ceccarelli
A1 J.S. Jackson
A1 S. Tauhid
A1 A. Arora
A1 J. Gorky
A1 E. Dell'Oglio
A1 A. Bakshi
A1 T. Chitnis
A1 S.J. Khoury
A1 H.L. Weiner
A1 C.R.G. Guttmann
A1 R. Bakshi
A1 M. Neema
YR 2012
UL http://www.ajnr.org/content/early/2012/03/29/ajnr.A3083.abstract
AB 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 controls. The other 3 methods overestimated the number of regions of GM loss in RRMS versus controls. 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. Abbreviations CCcross-correlationDARTELdiffeomorphic anatomical registration through exponentiated lie algebraEDSSExpanded Disability Status ScaleFPfalse-positivesGMgray matterLIlesion in-paintingMDEFTModified Driven Equilibrium Fourier TransformNCnormal controlsNMInormalized mutual informationRRMSrelapsing-remitting MSUSMunified segmentation modelVBMvoxel-based morphometry