Nonlinear cardiac deformation recovery from medical images

Authors:
Ken C. L. Wong;Linwei Wang;Heye Zhang;Pengcheng Shi
Affiliations:
Computational Biomedicine Laboratory, Rochester Institute of Technology, Rochester;Computational Biomedicine Laboratory, Rochester Institute of Technology, Rochester;Bioengineering Institute, University of Auckland, Auckland, New Zealand;Computational Biomedicine Laboratory, Rochester Institute of Technology, Rochester
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

To recover physiologically meaningful cardiac deformation from medical images, realistic physiological models are essential to constrain the recovery process, and a statistical filtering framework is required to couple the models and images according to their respective uncertainties. As realistic cardiac models are usually nonlinear, existing cardiac deformation recovery frameworks either ignore the statistical filtering part, or linearize the model and apply linear filtering techniques such as the extended Kalman filtering. This reduces the physiological plausibility and statistical optimality of the recovery results. In this paper, we propose a nonlinear cardiac deformation recovery framework with unscented Kalman filtering which preserves the intact system nonlinearity. Experiments were done on both synthetic data and magnetic resonance images to show the benefits and clinical relevance of our framework.