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AJNR Am J Neuroradiol. 2008 Feb;29(2):340-6. doi: 10.3174/ajnr.A0795.

Segmentation of subtraction images for the measurement of lesion change in multiple sclerosis.

Author information

1
Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.

Abstract

BACKGROUND AND PURPOSE:

Lesion volume change (LVC) assessment is essential in monitoring MS progression. LVC is usually measured by independently segmenting serial MR imaging examinations. Subtraction imaging has been proposed for improved visualization and characterization of lesion change. We compare segmentation of subtraction images (SSEG) with serial single time-point conventional segmentation (CSEG) by assessing the LVC relationship to brain atrophy and disease duration, as well as scan-rescan reproducibility and annual rates of lesion accrual.

MATERIALS AND METHODS:

Pairs of scans were acquired 1.5 to 4.7 years apart in 21 patients with multiple sclerosis (MS). Scan-rescan MR images were acquired within 30 minutes in 10 patients with MS. LVC was measured with CSEG and SSEG after coregistration and normalization. Coefficient of variation (COV) and Bland-Altman analyses estimated method reproducibility. Spearman rank correlations probed associations between LVC and other measures.

RESULTS:

Atrophy rate and net LVC were associated for SSEG (R = -0.446; P < .05) but not when using CSEG (R = -0.180; P = .421). Disease duration did not show an association with net lesion volume change per year measured by CSEG (R = -0.360; P = .11) but showed an inverse correlation with SSEG-derived measurements (R = -0.508; P < .05). Scan-rescan COV was lower for SSEG (0.98% +/- 1.55%) than for CSEG (8.64% +/- 9.91%).

CONCLUSION:

SSEG unveiled a relationship between T2 LVC and concomitant brain atrophy and demonstrated significantly higher measurement reproducibility. SSEG, a promising tool providing detailed analysis of subtle alterations in lesion size and intensity, may provide critical outcome measures for clinical trials of novel treatments, and may provide further insight into progression patterns in MS.

PMID:
18272569
DOI:
10.3174/ajnr.A0795
[Indexed for MEDLINE]
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