IEEE Trans Med Imaging. 2014 Oct;33(10):1931-8. doi: 10.1109/TMI.2014.2326041. Epub 2014 May 22.
Monte Carlo (MC) simulation provides a flexible and robust framework to efficiently evaluate and optimize image processing methods in emission tomography. In this work we present Brain-VISET (Voxel-based Iterative Simulation for Emission Tomography), a method that aims to simulate realistic [ (99m) Tc]-SPECT and [ (18) F]-PET brain databases by including anatomical and functional information. To this end, activity and attenuation maps generated using high-resolution anatomical images from patients were used as input maps in a MC projector to simulate SPECT or PET sinograms. The reconstructed images were compared with the corresponding real SPECT or PET studies in an iterative process where the activity inputs maps were being modified at each iteration. Datasets of 30 refractory epileptic patients were used to assess the new method. Each set consisted of structural images (MRI and CT) and functional studies (SPECT and PET), thereby allowing the inclusion of anatomical and functional variability in the simulation input models. SPECT and PET sinograms were obtained using the SimSET package and were reconstructed with the same protocols as those employed for the clinical studies. The convergence of Brain-VISET was evaluated by studying the behavior throughout iterations of the correlation coefficient, the quotient image histogram and a ROI analysis comparing simulated with real studies. The realism of generated maps was also evaluated. Our findings show that Brain-VISET is able to generate realistic SPECT and PET studies and that four iterations is a suitable number of iterations to guarantee a good agreement between simulated and real studies.
蒙特卡罗 (MC) 模拟为评估和优化发射断层成像中的图像处理方法提供了一个灵活且强大的框架。在这项工作中，我们提出了 Brain-VISET（基于体素的发射断层成像迭代模拟），这是一种旨在通过包含解剖学和功能信息来模拟逼真的 [ (99m)Tc]-SPECT 和 [ (18)F]-PET 脑数据库的方法。为此，使用来自患者的高分辨率解剖图像生成的活动和衰减图作为 MC 投影仪中的输入图，以模拟 SPECT 或 PET 正弦图。在迭代过程中，将重建图像与相应的真实 SPECT 或 PET 研究进行比较，在该过程中，在每次迭代时都会修改活动输入图。使用 30 名难治性癫痫患者的数据集来评估新方法。每个数据集都包含结构图像（MRI 和 CT）和功能研究（SPECT 和 PET），从而可以在模拟输入模型中包含解剖学和功能变异性。使用 SimSET 包获得 SPECT 和 PET 正弦图，并使用与临床研究相同的协议进行重建。通过研究相关性系数、图像直方图和比较模拟与真实研究的 ROI 分析的迭代过程中的行为来评估 Brain-VISET 的收敛性。生成图的逼真度也得到了评估。我们的发现表明，Brain-VISET 能够生成逼真的 SPECT 和 PET 研究，并且四个迭代是保证模拟研究和真实研究之间良好一致性的合适迭代次数。
