Time sequence diffeomorphic metric mapping and parallel transport track time-dependent shape changes

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• 作者：Anqi Qiu ; Marilyn Albert ; Laurent Younes ; Michael I. Miller
• 关键词：Time sequence registration ; Diffeomorphic metric mapping ; Parallel transport ; Longitudinal shape changes
• 刊名：NeuroImage
• 出版年：2009
• 出版时间：March 2009
• 年：2009
• 卷：45
• 期：1, Supplement 1
• 页码：S51-S60
• 全文大小：1109 K

文摘

Serial MRI human brain scans have facilitated the detection of brain development and of the earliest signs of neuropsychiatric and neurodegenerative diseases, monitoring disease progression, and resolving drug effects in clinical trials for preventing or slowing the rate of brain degeneration. To track anatomical shape changes in serial images, we introduce new point-based time sequence large deformation diffeomorphic metric mapping (TS-LDDMM) to infer the time flow of within-subject geometric shape changes that carry known observations through a period. Its Euler–Lagrange equation is generalized for anatomies whose shapes are characterized by point sets, such as landmarks, curves, and surfaces. The time-dependent momentum obtained from the TS-LDDMM encodes within-subject shape changes. For the purpose of across-subject shape comparison, we then propose a diffeomorphic analysis framework to translate within-subject deformation in a global template without incorporating across-subject anatomical variations via parallel transport technique. The analysis involves the retraction of the within-subject time-dependent momentum along the TS-LDDMM trajectory from each time to the baseline, the translation of the momentum in a global template, and the reconstruction of the TS-LDDMM trajectory starting from the global template.