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Interpolation of Longitudinal Shape and Image Data via Optimal Mass Transport

1Department of Electrical and Computer Engineering and the Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA.
2Departments of Computer Science and Applied Mathematics/Statistics, Stony Brook University, Stony Brook, New York, NY, USA.
3Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
Publication Date:
Proc Soc Photo Opt Instrum Eng
Volume Number:
Proc Soc Photo Opt Instrum Eng. 2014 Mar 21;9034:90342X.
PubMed ID:
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Gao Y., Zhu L-J., Bouix S., Tannenbaum A. Interpolation of Longitudinal Shape and Image Data via Optimal Mass Transport. Proc Soc Photo Opt Instrum Eng. 2014 Mar 21;9034:90342X. PMID: 25302008. PMCID: PMC4187117.
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Longitudinal analysis of medical imaging data has become central to the study of many disorders. Unfortunately, various constraints (study design, patient availability, technological limitations) restrict the acquisition of data to only a few time points, limiting the study of continuous disease/treatment progression. Having the ability to produce a sensible time interpolation of the data can lead to improved analysis, such as intuitive visualizations of anatomical changes, or the creation of more samples to improve statistical analysis. In this work, we model interpolation of medical image data, in particular shape data, using the theory of optimal mass transport (OMT), which can construct a continuous transition from two time points while preserving "mass" (e.g., image intensity, shape volume) during the transition. The theory even allows a short extrapolation in time and may help predict short-term treatment impact or disease progression on anatomical structure. We apply the proposed method to the hippocampus-amygdala complex in schizophrenia, the heart in atrial fibrillation, and full head MR images in traumatic brain injury.

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