Simultaneous Longitudinal Registration with Group-Wise Similarity Prior

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• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9123
• 期：1
• 页码：746-757
• 全文大小：1,067 KB
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• 作者单位：Greg M. Fleishman (17) (18)
  Boris A. Gutman (18)
  P. Thomas Fletcher (19)
  Paul M. Thompson (18)
  
  17. Department of Bioengineering, UC Los Angeles, Los Angeles, USA
  18. Imaging Genetics Center, LONI, Univeristy of Southern California, Los Angeles, USA
  19. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA
  
• 丛书名：Information Processing in Medical Imaging
• ISBN：978-3-319-19992-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Here we present an algorithm for the simultaneous registration of N longitudinal image pairs such that information acquired by each pair is used to constrain the registration of each other pair. More specifically, in the geodesic shooting setting for Large Deformation Diffeomorphic Metric Mappings (LDDMM) an average of the initial momenta characterizing the N transformations is maintained throughout and updates to individual momenta are constrained to be similar to this average. In this way, the N registrations are coupled and explore the space of diffeomorphisms as a group, the variance of which is constrained to be small. Our approach is motivated by the observation that transformations learned from images in the same diagnostic category share characteristics. The group-wise consistency prior serves to strengthen the contribution of the common signal among the N image pairs to the transformation for a specific pair, relative to features particular to that pair. We tested the algorithm on 57 longitudinal image pairs of Alzheimer’s Disease patients from the Alzheimer’s Disease Neuroimaging Initiative and evaluated the ability of the algorithm to produce momenta that better represent the long term biological processes occurring in the underlying anatomy. We found that for many image pairs, momenta learned with the group-wise prior better predict a third time point image unobserved in the registration.