(Sunday, 7/12/2020) [Eastern Time (GMT-4)]
Room: AAPM ePoster Library
Purpose:
To develop and validate a GPU-based Monte Carlo simulator for x-ray diffraction (XRD) imaging of cancerous tissue.
Methods:
A Monte Carlo simulation was developed using MCGPU and modified to enable user defined scatter distributions. User-defined form factors from experiments were adjusted to incorporate the molecular interference function for tissues (MIF) and used as input to MCGPU. All simulations were run at 60 keV mean x-ray energy with a pencil beam source and a flat panel detector and an object to detector distance of 40 cm or 11.5 cm depending on the system being modeled. Validation was performed against 3 calibration materials: aluminum, graphite and water, and against experimentally measured adipose using a commercial diffractometer (Bruxer AX3). In addition, clinical tissues – adipose, cancer, fibroglandular, and normal tissue -are compared to ground truth data. Quantitative comparisons were made for simulated vs theoretical attenuation coefficients the three calibration materials. Quantitative comparisons of peak height are made for the in-house Bruker system and the four clinical tissue samples.
Results:
Aluminum, graphite, and water showed attenuation coefficient differences of 4.6%, 3.9%, and 0.3%, respectively, against ground truth data. Simulated and experimental adipose tissue had a maximum peak location difference of 4.0%. Adipose, cancer, fibroglandular, and normal tissue had differences of 1.0%, 1.25%, 1.30%, and 0.20% compared to ground truth data with a mean difference of 0.938% and a standard deviation of 0.509. Average run time was found to be 22.34 seconds.
Conclusion:
The difference between simulation and ground truth calibration materials as well as simulation and experimental adipose tissue validate MC-GPU as a simulation toolkit to design and test clinical x-ray diffraction imaging systems. The location of maximum relative peak intensity of the cancer/adipose tissue demonstrate MCGPU can be used to build a clinical cancer detection system.
Keywords
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Taxonomy
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