Improving fluid registration through white matter segmentation in a twin study design

Chou,Yi-Yu, Lepore, Natasha, Brun, Caroline, Barysheva, Marina, McMahon, Katie, De Zubicaray, Greig I., Wright, Margaret J., Toga, Arthur W. and Thompson, Paul M. (2010). Improving fluid registration through white matter segmentation in a twin study design. In: Benoit M. Dawant and David R. Haynor, Progress in Biomedical Optics and Imaging. Proceedings of SPIE. Medical Imaging 2010: Image Processing, San Diego, CA, U.S.A., (). 14-16 February 2010. doi:10.1117/12.843642


Author Chou,Yi-Yu
Lepore, Natasha
Brun, Caroline
Barysheva, Marina
McMahon, Katie
De Zubicaray, Greig I.
Wright, Margaret J.
Toga, Arthur W.
Thompson, Paul M.
Title of paper Improving fluid registration through white matter segmentation in a twin study design
Conference name Medical Imaging 2010: Image Processing
Conference location San Diego, CA, U.S.A.
Conference dates 14-16 February 2010
Convener Kevin R. Cleary
Proceedings title Progress in Biomedical Optics and Imaging. Proceedings of SPIE
Journal name Medical Imaging 2010: Image Processing
Place of Publication Washington, DC, U.S.A.
Publisher SPIE-International Society of Optical Engineering
Publication Year 2010
Sub-type Poster
DOI 10.1117/12.843642
ISBN 9780819480248
ISSN 1605-7422
1042-4687
Editor Benoit M. Dawant
David R. Haynor
Volume 7623
Issue Part 1, Article number 76232X
Total pages 7
Collection year 2011
Language eng
Formatted Abstract/Summary
Robust and automatic non-rigid registration depends on many parameters that have not yet been systematically explored. Here we determined how tissue classification influences non-linear fluid registration of brain MRI. Twin data is ideal for studying this question, as volumetric correlations between corresponding brain regions that are under genetic control should be higher in monozygotic twins (MZ) who share 100% of their genes when compared to dizygotic twins (DZ) who share half their genes on average. When these substructure volumes are quantified using tensor-based morphometry, improved registration can be defined based on which method gives higher MZ twin correlations when compared to DZs, as registration errors tend to deplete these correlations. In a study of 92 subjects, higher effect sizes were found in cumulative distribution functions derived from statistical maps when performing tissue classification before fluid registration, versus fluidly registering the raw images. This gives empirical evidence in favor of pre-segmenting images for tensor-based morphometry.
©2010 COPYRIGHT SPIE--The International Society for Optical Engineering.
Keyword MRI
Registration
Tissue classification
Twin study
Q-Index Code EX
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes session Posters: Registration

 
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Created: Fri, 22 Oct 2010, 15:53:50 EST by Sandrine Ducrot on behalf of Centre for Advanced Imaging