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INFORMATION THEORETIC MEASURES FOR PET IMAGE
RECONSTRUCTION AND NON-RIGID IMAGE REGISTRATION
by
Sangeetha Somayajula
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
December 2009
Copyright 2009 Sangeetha Somayajula
Object Description
| Title | Information theoretic measures for PET image reconstruction and non-rigid image registration |
| Author | Somayajula, Sangeetha |
| Author email | somayaju@usc.edu; sangsom@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2009-09-02 |
| Date submitted | 2009 |
| Restricted until | Unrestricted |
| Date published | 2009-10-14 |
| Advisor (committee chair) | Leahy, Richard M. |
| Advisor (committee member) |
Nayak, Krishna S. Singh, Manbir |
| Abstract | We explore the use of information theoretic measures for positron emission tomography (PET) image reconstruction and for multi-modality non-rigid registration.; PET is a functional imaging modality based on imaging the uptake of a radioactive tracer. PET images are typically of low resolution and are accompanied by high resolution anatomical images such as CT or MR for localization of activity. PET tracer uptake typically results in a spatial density that is highly correlated to the anatomical morphology. The incorporation of this correlated anatomical information can potentially improve the quality of low resolution PET images. We propose using priors based on information theoretic similarity measures to incorporate anatomical information in PET reconstruction. We compare and evaluate the use of mutual information (MI) and joint entropy (JE) between feature vectors extracted from the anatomical and PET images as priors in PET reconstruction. These feature vectors are defined using scale-space theory such that they emphasize prominent boundaries in the anatomical and functional images, and attach less importance to detail and noise that is less likely to be correlated in the two images. We present an efficient method of computing these priors and their derivatives based on fast Fourier transforms that reduce the complexity of their convolution-like expressions. Through simulations and clinical data reconstructions, we evaluate the performance of MI and JE based priors in comparison to a quadratic prior, which does not use any anatomical information. We also apply these priors to the problem of positron range correction. Positron range is the distance traveled by a positron before annihilation, thereby causing a blurring effect in the reconstructed image and limiting its resolution. We use information theoretic priors in conjunction with a system model that incorporates positron range by modeling it as a spatially invariant blurring that is truncated at the boundary of the imaging volume. We present phantom simulation and real data results comparing these priors to the range corrected system model with quadratic prior.; Small animal non-rigid registration is especially challenging because of the presence of rigid structures like the skeleton within non-rigid soft tissue. We present two approaches to multi-modality imaging that can be applied to clinical as well as pre-clinical images. First, we describe a non-parametric scale-space approach to MI based non-rigid small animal image registration. In this application, the goal is to simultaneously align global structure as well as detail in the images. We present results based on CT images obtained from two time points of a longitudinal mouse study that demonstrate that this approach aligns both skeleton and soft tissue better than a commonly used hierarchical approach. Second, we explore an alternative formulation that uses the log likelihood of the reference image (target) given the image to be registered (template) as a similarity metric wherein the likelihood is modeled using Gaussian mixture models (GMM). Using the GMM formulation reduces the density estimation step to that of estimating the GMM parameters. These parameters are small in number for images that have few regions of distinct intensities, such as brain or microCT images. This approach is more robust than the non-parametric MI based approach because of reduced overall dimensionality of the problem and more robust and accurate density estimation. We present comparisons of the non-parametric MI based approach to that of the GMM conditional likelihood based approach through intra-modality brain and multi-modality mouse images.; Finally, we present future directions for the work described in this dissertation. |
| Keyword | biomedical imaging; joint entropy; multi-modality imaging; mutual information; PET reconstruction; image registration |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m2661 |
| Rights | Somayajula, Sangeetha |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Somayajula-3273 |
| Archival file | uscthesesreloadpub_Volume56/etd-Somayajula-3273.pdf |
Description
| Title | Page 1 |
| Full text | INFORMATION THEORETIC MEASURES FOR PET IMAGE RECONSTRUCTION AND NON-RIGID IMAGE REGISTRATION by Sangeetha Somayajula A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) December 2009 Copyright 2009 Sangeetha Somayajula |
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