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      Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.

      1 , , ,
      Medical image analysis
      Elsevier BV

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          Abstract

          One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.

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          Author and article information

          Journal
          Med Image Anal
          Medical image analysis
          Elsevier BV
          1361-8423
          1361-8415
          Feb 2008
          : 12
          : 1
          Affiliations
          [1 ] Department of Radiology, University of Pennsylvania, 3600 Market Street, Philadelphia, PA 19104, United States. avants@grasp.cis.upenn.edu
          Article
          S1361-8415(07)00060-6 NIHMS41509
          10.1016/j.media.2007.06.004
          2276735
          17659998
          163af76b-622b-4f9c-9880-60e95413936c
          History

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