Purpose: Our group has shown X-ray diffraction (XRD) imaging for thin samples, however, its applicability to thick samples for pathology diagnostics, small animals, and potentially in-vivo applications has not been explored. In thin samples, single scatter events dominate, 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 in medical XRD imaging and explore methods of mitigation or use of multiple scatter.
Methods: We utilize a Monte Carlo GPU-accelerated photon simulation code capable of modeling medical XRD imaging. Our group has previously validated the code and added additional form factors allowing for the implementation of the molecular interference function. We model a notional pencil beam diffraction system consistent with current mammography systems. The detector is 20x20 cm, containing 300x300 pixels and, has 100% efficiency. Multiple object geometries (e.g., cylinders, rectangles, cubes) with different aspect ratios and materials (e.g., water, graphite, adipose, bone) are represented as simulated phantoms. We independently vary the object's geometric extent and optical thickness in order to separately analyze the impact of each parameter.
Results: Examination of thick tissue samples showed the increased intensity of multiple scatter present in overall scatter profiles. As energy and tissue type varied, scatter profiles reflected changes in overall scatter intensity and subsequently coherent, Compton, and multiple scatter intensity. We further evaluate the system’s appropriateness for medical in-vivo imaging.
Conclusion: This work indicates the potential for significant advances in medical XRD imaging. We provide evidence that XRD imaging on thick samples is feasible under the proper conditions. Overall, we further our understanding of the role of multiple scatter in medical XRD imaging and provide a framework for analyzing and implementing XRD imaging on thick samples.