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

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), a...

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Published in:Journal of magnetic resonance imaging Vol. 32; no. 1; pp. 223 - 228
Main Authors: Chard, Declan T., Jackson, Jonathan S., Miller, David H., Wheeler-Kingshott, Claudia A.M.
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.07.2010
Subjects:
brain
Brain - anatomy & histology
Computer Simulation
error correction
Humans
Image Processing, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Magnetic Resonance Imaging - methods
MRI
multiple sclerosis
Reference Values
segmentation errors
white matter lesions
ISSN:1053-1807, 1522-2586, 1522-2586
Online Access:Get full text
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Summary: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 ≈2.3%, and reduce WM fractions by ≈3.6%. Lesion filling reduced these errors to ≈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. J. Magn. Reson. Imaging 2010. © 2010 Wiley‐Liss, Inc.
Bibliography:ark:/67375/WNG-CDMRZ6F5-J
istex:B32C615C7EBC4F23B0978792BA56047B6189879D
ArticleID:JMRI22214
The first two authors contributed equally to this work.
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1053-1807
1522-2586
1522-2586
DOI:10.1002/jmri.22214