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SIMULATION AND DEVELOPMENT OF A CLINICAL ANALYZER-BASED IMAGING SYSTEM
Title
SIMULATION AND DEVELOPMENT OF A CLINICAL ANALYZER-BASED IMAGING SYSTEM
Creator
Majidi, Keivan
Date
2013, 2013-12
Description
The analyzer-based phase-sensitive X-ray imaging method (ABI) is emerging as a potential alternative to conventional radiography. ABI...
Show moreThe analyzer-based phase-sensitive X-ray imaging method (ABI) is emerging as a potential alternative to conventional radiography. ABI simultaneously generates a number of planar images containing information about scattering, refraction and absorption properties of the object. These parametric images are acquired by sampling the angular intensity profile (AIP) of an X-ray beam passing through the object at different positions of the analyzer crystal. Like many of the modern imaging techniques, ABI is a computed imaging method (meaning that the images are calculated from raw data). Therefore, the noise in ABI depends on the imaging conditions such as source flux, number of the analyzer positions, and the analyzer positions themselves as well as on the estimation method of the parameters. In the first part of this thesis, we use the Cramer-Rao lower bound to quantify the noise in ABI images and then investigate the effect of different analyzer-sampling strategies on this bound. The CRLB is the minimum bound for the variance of an unbiased estimator and defines the best noise performance that one can obtain regardless of which estimation method is used to estimate ABI parametric images. We will then use this bound to evaluate three ABI methods: Multiple-Image Radiography (MIR), Diffraction Enhanced Imaging (DEI) and Scatter Diffraction Enhanced Imaging (S-DEI). The proposed methodology can be used to evaluate any other ABI parametric image estimation technique. Synchrotron radiation has been the main source for experimental ABI and developing its methodologies, therefore the ABI application to clinical imaging has been very limited. It is inevitable to use conventional X-ray sources for ABI in order to utilize xii the technique in the clinical applications, however, due to the limited intensity of these sources and their finite source size, developing such systems is very challenging. In the second part of this thesis, we use computer simulations to understand the above challenges better. We measure the properties of this imaging system such as flux and point-spread function for various design parameters and discuss how to find an “optimal” setup based on these properties. The optimality of an imaging setup depends on the specific application that one wants to perform using the system; however, the results and discussions in this section layouts a design procedure for clinical ABI systems. In the last part of this thesis we review the steps we took in the Advanced X-ray Imaging Laboratory (AXIL) toward developing a clinical ABI system.
PH.D in Electrical Engineering, December 2013
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