Lee, Huai Ping. Simulation-based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis. University of North Carolina at Chapel Hill, 2012. https://doi.org/10.17615/7mdx-ps96
Lee, H. (2012). Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis. University of North Carolina at Chapel Hill. https://doi.org/10.17615/7mdx-ps96
Lee, Huai Ping. 2012. Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis. University of North Carolina at Chapel Hill. https://doi.org/10.17615/7mdx-ps96
Affiliation: College of Arts and Sciences, Department of Computer Science
Elasticity parameter estimation is essential for generating accurate and controlled simulation results for computer animation and medical image analysis. However, finding the optimal parameters for a particular simulation often requires iterations of simulation, assessment, and adjustment and can become a tedious process. Elasticity values are especially important in medical image analysis, since cancerous tissues tend to be stiffer. Elastography is a popular type of method for finding stiffness values by reconstructing a dense displacement field from medical images taken during the application of forces or vibrations. These methods, however, are limited by the imaging modality and the force exertion or vibration actuation mechanisms, which can be complicated for deep-seated organs. In this thesis, I present a novel method for reconstructing elasticity parameters without requiring a dense displacement field or a force exertion device. The method makes use of natural deformations within the patient and relies on surface information from segmented images taken on different days. The elasticity value of the target organ and boundary forces acting on surrounding organs are optimized with an iterative optimizer, within which the deformation is always generated by a physically-based simulator. Experimental results on real patient data are presented to show the positive correlation between recovered elasticity values and clinical prostate cancer stages. Furthermore, to resolve the performance issue arising from the high dimensionality of boundary forces, I propose to use a reduced finite element model to improve the convergence of the optimizer. To find the set of bases to represent the dimensions for forces, a statistical training based on real patient data is performed. I demonstrate the trade-off between accuracy and performance by using different numbers of bases in the optimization using synthetic data. A speedup of more than an order of magnitude is observed without sacrificing too much accuracy in recovered elasticity.