Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes.

摘要

PURPOSE: To develop an automated lesion-filling technique (LEAP; LEsion Automated Preprocessing) that would reduce lesion-associated brain tissue segmentation bias (which is known to affect automated brain gray [GM] and white matter [WM] tissue segmentations in people who have multiple sclerosis), and a WM lesion simulation tool with which to test it. MATERIALS AND METHODS: Simulated lesions with differing volumes and signal intensities were added to volumetric brain images from three healthy subjects and then automatically filled with values approximating normal WM. We tested the effects of simulated lesions and lesion-filling correction with LEAP on SPM-derived tissue volume estimates. RESULTS: GM and WM tissue volume estimates were affected by the presence of WM lesions. With simulated lesion volumes of 15 mL at 70% of normal WM intensity, the effect was to increase GM fractional (relative to intracranial) volumes by approximately 2.3%, and reduce WM fractions by approximately 3.6%. Lesion filling reduced these errors to approximately 0.1%. CONCLUSION: The effect of WM lesions on automated GM and WM volume measures may be considerable and thereby obscure real disease-mediated volume changes. Lesion filling with values approximating normal WM enables more accurate GM and WM volume measures and should be applicable to structural scans independently of the software used for the segmentation.