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Development and Validation of a GPU Based Monte Carlo Simulator for Diffraction Imaging in Cancer

O Fasina1*, A Kapadia2, (1) ,Durham, NC, (2) Duke University Medical Center, Durham, NC


(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library


To develop and validate a GPU-based Monte Carlo simulator for x-ray diffraction (XRD) imaging of cancerous tissue.


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.


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.


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.


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