Journal
JOURNAL OF MAGNETIC RESONANCE IMAGING
Volume 15, Issue 2, Pages 203-209Publisher
WILEY-BLACKWELL
DOI: 10.1002/jmri.10053
Keywords
multiple sclerosis; magnetic resonance imaging; image processing, computer-assisted; brain, aging; brain, white matter; magnetic resonance imaging, volume measurement
Funding
- NCRR NIH HHS [P41 RR13218-01] Funding Source: Medline
- NIA NIH HHS [AGO9675, P01 AG0495316] Funding Source: Medline
- NIMH NIH HHS [2R44MH57200-002] Funding Source: Medline
- NINDS NIH HHS [R01 NS35142] Funding Source: Medline
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Purpose: To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI) measurement of brain white matter signal abnormalities (WMSA). Materials and Methods: WMSA segmentation was performed on pairs of whole brain scans from 20 patients with multiple sclerosis (MS) and 10 older subjects who were positioned and imaged twice within 30 minutes. Radiologist outlines of WMSA on 20 sections from 16 patients were compared with the corresponding results of each segmentation method. Results: The segmentation method combining expectation-maximization (EM) tissue segmentation, template-driven segmentation (TDS), and partial volume effect correction (PVEC) demonstrated the highest accuracy (the absolute value of the Z-score was 0.99 for both groups of subjects), as well as high interscan reproducibility (repeatability coefficient was 0.68 mL in MS patients and 1.49 mL in aging subjects). Conclusion: The addition of TDS to the EM segmentation and PVEC algorithms significantly improved the accuracy of WMSA volume measurements, while also improving measurement reproducibility.
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