Greenberg, Joel AFerguson, Kyle2023-06-082023https://hdl.handle.net/10161/27791<p>Our group has shown X-ray diffraction imaging for thin samples, however, its applicability to thick samples for pathology diagnostics, small animal imaging, and potentially in-vivo applications has yet to be explored. Single scatter events dominate in tissues on the order of a few millimeters, and solving the inverse problem of scatter localization is straightforward. As the sample thickness increases, multiple scatter and geometric blurring effects become important. We look to quantify the role of optical and geometric object thickness, explore the effects of tumor size and location, and quantify the role of varying breast density in medical X-ray diffraction imaging. Using MC-GPU, a Monte Carlo GPU-accelerated photon simulation code, we simulate X-ray diffraction studies for various energies with phantoms combining a variety of object compositions, tumor sizes, tumor locations, and distances from the detector. Previous validation of MC-GPU conducted by our group has added additional form factors allowing for the implementation of the molecular interference function. A notional pencil beam diffraction system is modeled with a 2D grid of pixels approximating a 2D flat panel, noise-free, and 100% efficient detector. Results are analyzed using different metrics, including signal-to-background, signal-to-noise, and signal-to-multiple scatter. Analysis showed optimal energy ranges for thick tissue diffraction simulations, non-significant effects of tumor location in the object, and varying breast composition playing a pivotal role in tumor detectability. This work has indicated the potential for significant advances in medical X-ray imaging, specifically in the application to in vivo and thick tissue imaging. We provide evidence that X-ray diffraction imaging on thick tissue samples is feasible under proper conditions. Overall, we further our understanding of the role of thick tissues, tumor location, and tumor size in medical X-ray diffraction imaging and provide a framework for analyzing and implementing XRD imaging on thick samples. </p>Medical imagingMaster's thesis