Optimization of X-Ray Diffraction Imaging of Medical Specimens by Monte Carlo
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Our research group has previously described the development and testing of a coherent-scatter spectral imaging system for identification of cancer using surrogate phantoms, formalin-fixed pathology tissues and, more recently, surgically resected breast tumor. Here we present the implementation of a Monte-Carlo simulation tool for optimization of the imaging system.
MC-GPU, a GPU-enabled Monte Carlo software was modified and used to simulate X-ray diffraction experiments for combinations of X-ray spectra (tungsten and molybdenum anode), kV (15-150), filtration (material and thickness) and phantom geometry and material (normal, adipose, fibroglandular, and cancerous breast tissue). For each combination, a simulated measurement of contrast-to-noise (CNR), signal strength and object detectability were assessed.
Examination of Monte Carlo simulations showed optimal spectrum characterization strategies that exploit spectral and filter characteristics to increase material identification probabilities via momentum transfer measurement. Increased detectability was shown with molybdenum energy spectra, and a higher CNR metric was observed to show better pathological assessments and findings of cancer.
This work demonstrates the utility of Monte Carlo methods and MCGPU in optimizing coherent scatter imaging systems and can be used to provide insightful information regarding the design of coherent scatter imaging systems for material classification breast tissue types.
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Rights for Collection: Masters Theses