Image Guided Respiratory Motion Analysis: Time Series and Image Registration.

Time Series Analysis;Image Registration;Statistical Signal Processing;Robust Estimation;Stochastic Approximation;Electrical Engineering;Radiology;Engineering;Health Sciences;Electrical Engineering: Systems

Ruan, DanMeyer, Charles R. ;

University of Michigan

关键词: Time Series Analysis;    Image Registration;    Statistical Signal Processing;    Robust Estimation;    Stochastic Approximation;    Electrical Engineering;    Radiology;    Engineering;    Health Sciences;    Electrical Engineering: Systems;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/60673/druan_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

The efficacy of Image guided radiation therapy (IGRT) systems relies on accurately extracting, modeling and predicting tumor movement with imaging techniques. This thesisinvestigates two key problems associated with such systems: motion modeling and imageprocessing. For thoracic and upper abdominal tumors, respiratory motion is the dominantfactor for tumor movement. We have studied several special structured time series analysis techniques to incorporate the semi-periodicity characteristics of respiratory motion.The proposed methods are robust towards large variations among fractions and populations; the algorithms perform stably in the presence of sparse radiographic observationswith noise. We have proposed a subspace projection method to quantitatively evaluate thesemi-periodicity of a given observation trace; a nonparametric local regression approachfor real-time prediction of respiratory motion; a state augmentation scheme to model hysteresis; and an ellipse tracking algorithm to estimate the trend of respiratory motion inreal time. For image processing, we have focused on designing regularizations to accountfor prior information in image registration problems. We investigated a penalty function design that accommodates tissue-type-dependent elasticity information. We studied a class of discontinuity preserving regularizers that yield smooth deformation estimatesin most regions, yet allow discontinuities supported by data. We have further proposed adiscriminate regularizer that preserves shear discontinuity, but discourages folding or vacuum generating flows. In addition, we have initiated a preliminary principled study on thefundamental performance limit of image registration problems. We proposed a statisticalgenerative model to account for noise effect in both source and target images, and investigated the approximate performance of the maximum-likelihood estimator correspondingto the generative model and the commonly adopted M-estimator. A simple example suggests that the approximation is reasonably accurate.Our studies in both time series analysis and image registration constitute essentialbuilding-blocks for clinical applications such as adaptive treatment. Besides their theoretical interests, it is our sincere hope that with further justifications, the proposed techniqueswould realize its clinical value, and improve the quality of life for patients.