Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy

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.