Shaker Kian, Shi Linxi, Hsieh Scott, Swaby Akyl, Abbaszadeh Shiva, Wang Adam S
Department of Radiology, Stanford University, Stanford, CA 94305, USA.
Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12925. doi: 10.1117/12.3006250. Epub 2024 Apr 1.
Generating realistic radiographs from CT is mainly limited by the native spatial resolution of the latter. Here we present a general approach for synthesizing high-resolution digitally reconstructed radiographs (DRRs) from an arbitrary resolution CT volume. Our approach is based on an upsampling framework where tissues of interest are first segmented from the original CT volume and then upsampled individually to the desired voxelization (here ~1 mm → 0.2 mm). Next, we create high-resolution 2D tissue maps by cone-beam projection of individual tissues in the desired radiography direction. We demonstrate this approach on a coronary artery calcium (CAC) patient CT scan and show that our approach preserves individual tissue volumes, yet enhances the tissue interfaces, creating a sharper DRR without introducing artificial features. Lastly, we model a dual-layer detector to synthesize high-resolution dual-energy (DE) anteroposterior and lateral radiographs from the patient CT to visualize the CAC in 2D through material decomposition. On a general level, we envision that this approach is valuable for creating libraries of synthetic yet realistic radiographs from corresponding large CT datasets.
从CT生成逼真的X光片主要受限于CT本身的空间分辨率。在此，我们提出一种从任意分辨率的CT容积数据合成高分辨率数字重建X光片（DRR）的通用方法。我们的方法基于一个上采样框架，首先从原始CT容积数据中分割出感兴趣的组织，然后将其分别上采样到所需的体素化分辨率（此处为~1毫米→0.2毫米）。接下来，通过在所需的X光摄影方向上对单个组织进行锥束投影，创建高分辨率的二维组织图。我们在冠状动脉钙化（CAC）患者的CT扫描上展示了这种方法，结果表明我们的方法能够保留单个组织的体积，同时增强组织界面，从而创建出更清晰的DRR，且不会引入人为特征。最后，我们构建了一个双层探测器模型，从患者的CT数据合成高分辨率的双能（DE）前后位和侧位X光片，通过物质分解在二维层面上可视化CAC。总体而言，我们设想这种方法对于从相应的大型CT数据集中创建合成但逼真的X光片库具有重要价值。
