X-ray diffraction (XRD) imaging yields spatially resolved, material-specific information, which can aid medical diagnosis and inform treatment. In this work we used simulations to analyze the utility of fan beam coded aperture XRD imaging for fast, high-resolution scatter imaging of biospecimens for tissue assessment. To evaluate the proposed system’s utility in a specific task, we employed a deterministic model to produce simulated data from biologically realistic breast tissue phantoms and model-based reconstruction to recover a spatial map of the XRD signatures throughout the phantoms. We found an XRD spatial resolution of ≈1 mm with a mean reconstructed spectral accuracy of 0.980.01 for a simulated 1נ150 mm² fan beam operating at 160 kVp, 10 mA, and 4.5 s exposures. A classifier for cancer detection was developed utilizing cross-correlation of XRD spectra against a spectral library, with a receiver operating characteristic curve with an area under the curve value of 0.972. Our results indicated a potential diagnostic modality that could aid in tasks ranging from analysis of ex-vivo pathology biospecimens to intraoperative cancer margin assessment, motivating future work to develop an experimental system while enabling the development of improved algorithms for imaging and tissue analysis-based classification performance.

X 射线衍射 (XRD) 成像产生空间分辨的、特定于材料的信息，可以帮助医疗诊断和告知治疗。在这项工作中，我们使用模拟来分析扇形束编码孔径 XRD 成像在生物样本的快速、高分辨率散射成像中用于组织评估的效用。为了评估所提出的系统在特定任务中的效用，我们采用了确定性模型从生物学逼真的乳房组织模型和基于模型的重建中生成模拟数据，以恢复整个模型中 XRD 特征的空间图。我们发现 XRD 空间分辨率约为 1 mm，对于模拟 1נ150 mm ²的平均重建光谱精度为 0.980.01在 160 kVp、10 mA 和 4.5 s 曝光下运行的扇形光束。利用 XRD 光谱与光谱库的互相关开发了一种用于癌症检测的分类器，其接收器操作特征曲线的曲线下面积为 0.972。我们的结果表明了一种潜在的诊断方式，可以帮助完成从离体病理生物样本分析到术中癌症边缘评估等任务，激励未来的工作开发实验系统，同时能够开发改进的基于成像和组织分析的分类算法表现。