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Proceedings Paper

Interpolation of longitudinal shape and image data via optimal mass transport
Author(s): Yi Gao; Liang-Jia Zhu; Sylvain Bouix; Allen Tannenbaum
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Paper Abstract

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.

Paper Details

Date Published: 21 March 2014
PDF: 5 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90342X (21 March 2014); doi: 10.1117/12.2043282
Show Author Affiliations
Yi Gao, The Univ. of Alabama at Birmingham (United States)
Liang-Jia Zhu, Stony Brook Univ. (United States)
Sylvain Bouix, Harvard Medical School (United States)
Allen Tannenbaum, Stony Brook Univ. (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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