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AJNR Am J Neuroradiol. 2004 Jun-Jul;25(6):985-96.

Whole-brain atrophy in multiple sclerosis measured by automated versus semiautomated MR imaging segmentation.

Author information

1
Buffalo Neuroimaging Analysis Center, University at Buffalo, State University of New York, Buffalo, NY, USA.

Abstract

BACKGROUND AND PURPOSE:

Semiautomated and automated methods are used to measure whole-brain atrophy in multiple sclerosis (MS), but their comparative reliability, sensitivity, and validity are unknown.

METHODS:

Brain parenchymal fraction (BPF) was measured in patients with MS (n = 52) and healthy control subjects (n = 17) by four methods: semiautomated or automated segmentation and 2D or 3D pulse sequences. Linear measures of atrophy, whole-brain lesion volumes, and clinical data were used to explore validity.

RESULTS:

The 2D automated method yielded unreliable segmentation and was discarded. The three other BPF methods produced data that were highly intercorrelated and indistinguishable by analysis of variance. In the MS group, semiautomated (2D: 0.84 +/- 0.04, P <.001; 3D: 0.84 +/- 0.05, P =.04) and automated 3D (0.83 +/- 0.05, P =.002) BPFs were lower than controls (semiautomated 2D: 0.88 +/- 0.02; 3D: 0.88 +/- 0.03; automated 3D: 0.88 +/- 0.03). In the MS group, the semiautomated (r = -.79 to -.82) and automated 3D (r = -.81) BPFs inversely correlated with third ventricular width and showed similarly robust correlations with the bicaudate ratio (all r = -.74). The semiautomated and automated BPFs showed similar, moderate correlations with T1 hypointense and FLAIR hyperintense lesion volume, physical disability (Expanded Disability Status Scale) score, and disease duration and similar differences between secondary progressive and relapsing-remitting patients. The intraobserver, interobserver, and test-retest reliability was somewhat higher for the automated than for the semiautomated methods.

CONCLUSION:

These automated and semiautomated measures of whole-brain atrophy provided similar and nearly interchangeable data regarding MS. They discriminated MS from healthy individuals and showed similar relationships to established disease variables.

PMID:
15205136
[Indexed for MEDLINE]
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