Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Medical Biophysics


Dr. Ian A. Cunningham

Second Advisor

Dr. Jerry J. Battista

Third Advisor

Dr. Terry Peters


X-ray detectors are an integral part of any x-ray imaging system. In order to maximize system performance, and hence image quality, signal and noise must be efficiently transferred from input to output. Ideally, an x-ray detector should preserve the input signal-to-noise ratio (SNR). However, in reality, various physical processes within the x-ray detector degrade SNR, which consequently results in lower image quality for a given x-ray imaging dose. The goal of this work is to understand how signal and noise correlations limit the performance of diagnostic x-ray detectors, especially those used in high-resolution imaging applications, such as mammography and micro computed tomography (CT). The fundamental spatial resolution and SNR limits caused by signal and noise correlations associated with x-ray interactions was determined using Monte Carlo simulations of the absorbed energy in common x-ray detector materials as a function of incident energy and converter thickness. These fundamental limits help identify potential performance bottlenecks in existing detectors and also serve as target benchmarks for future designs. Theoretical models of signal and noise transfer through the photoelectric effect and CT filtered backprojection algorithm were developed using a cascaded systems analysis to analytically predict how signal and noise correlations affect detector performance and CT image quality, respectively. This work provides x-ray detector manufacturers and imaging scientists (i) a priori knowledge of the fundamental barriers of detector performance, and (ii) “tools” necessary for the design and optimization of radiography and CT based imaging systems. These contributions will not only save time, money and resources, but will ultimately lead to x-ray detectors with higher SNR efficiency, which in turn, may lead to better image quality (greater diagnostic accuracy) and/or lower patient dose (lower cancer risk).



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