Room: Exhibit Hall | Forum 8
Purpose: We have 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 an optimization study for the imaging system via Monte Carlo methods.
Methods: MC-GPU, a GPU-enabled Monte Carlo software was modified and validated to provide energy-discriminating signal information from simulated x-ray scatter experiments. After validation, X-ray diffraction simulations were modeled and executed for combinations of X-ray spectra (tungsten and molybdenum anode), kV (20-150), 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 at each spectral energy-bin.
Results: Examination of Monte Carlo simulations show optimal spectrum and x-ray binning strategies that exploit spectral and filter characteristics to increase material identification probabilities via momentum transfer measurement. Increased detectability was shown to be at or near anode k-edges while using spectrum filter strategies that filter higher than binned x-ray energies while maintaining signal.
Conclusion: We Optimized imaging system for breast cancer identification in pathology. An optimal theoretical spectrum and detector-binning strategy was observed for use in x-ray diffraction imaging. This work demonstrates the utility of Monte Carlo 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.
Coherent Scatter, Monte Carlo, Optimization