Shaker Kian, Larsson Jakob C, Hertz Hans M
Biomedical and X-Ray Physics, Department of Applied Physics, KTH Royal Institute of Technology/AlbaNova 106 91 Stockholm, Sweden.
Biomed Opt Express. 2019 Jul 3;10(8):3773-3788. doi: 10.1364/BOE.10.003773. eCollection 2019 Aug 1.
X-ray fluorescence (XRF) tomography from nanoparticles (NPs) shows promise for high-spatial-resolution molecular imaging in small-animals. Quantitative reconstruction algorithms aim to reconstruct the true distribution of NPs inside the small-animal, but so far there has been no feasible way to predict signal levels or evaluate the accuracy of reconstructions in realistic scenarios. Here we present a GPU-based computational model for small-animal XRF tomography. The unique combination of a highly accelerated Monte Carlo tool combined with an accurate small-animal phantom allows unprecedented realistic full-body simulations. We use this model to simulate our experimental system to evaluate the quantitative performance and accuracy of our reconstruction algorithms on large-scale organs as well as mm-sized tumors. Furthermore, we predict the detection limits for sub-mm tumors at realistic NP concentrations. The computational model will be a valuable tool for optimizing next-generation experimental arrangements and reconstruction algorithms.
基于纳米颗粒（NPs）的X射线荧光（XRF）断层扫描技术在小动物高空间分辨率分子成像方面展现出了前景。定量重建算法旨在重建小动物体内NPs的真实分布，但到目前为止，尚无可行的方法来预测信号水平或评估实际场景中重建的准确性。在此，我们提出了一种基于GPU的小动物XRF断层扫描计算模型。高度加速的蒙特卡罗工具与精确的小动物模型的独特结合，实现了前所未有的逼真全身模拟。我们使用该模型模拟我们的实验系统，以评估重建算法在大型器官以及毫米级肿瘤上的定量性能和准确性。此外，我们预测了在实际NP浓度下亚毫米级肿瘤的检测限。该计算模型将成为优化下一代实验装置和重建算法的宝贵工具。
