Composite system modelling for high accuracy brain PET image reconstruction using GATE

M. Belzunce, A. Reader
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Abstract

High resolution and good quantification is needed in specific regions of the brain in a number of PET brain imaging applications. An improvement in the spatial resolution and in the quantification of the tracer uptake can be achieved by using statistical reconstruction methods with an accurate model of the scanner acquisition process. This model is represented by a system response matrix and needs to include all the factors that contribute to the degradation of the reconstructed images. Monte Carlo simulations are the best method to model the complex physical processes involved in PET, but they have an extremely high computational cost and the system matrix needs to be recomputed for every new scan. Furthermore, for 3D PET the system matrix can have billions of elements, therefore at present it is impossible to store in memory during the iterative reconstruction. Consequently, on-the-fly Monte Carlo modelling of the system matrix has been previously proposed by other authors, where a Monte Carlo simulation is used in the forward projector and a simpler analytic model in the backprojector. In this work, we propose a different approach, where a composite system matrix is used, with a complete Monte Carlo model computed with GATE for a small subregion of the field of view and a simpler analytic model for the voxels outside that region. We evaluated the feasibility of the method using 2D simulations of a striatum phantom and a brain phantom. For each case, a Monte Carlo system matrix was generated with GATE for a subregion centred in the striatum. The brain simulations were reconstructed using the proposed method and compared with the standard reconstruction used clinically, with and without resolution modelling. For the striatum phantom, the use of a GATE system matrix showed an improvement of the reconstructed image, where a better definition of the structures in the striatum region was observed. For the case of the brain phantom, where the composite system matrix is used, an improvement was also observed but more limited compared with the pure GATE system matrix.
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基于GATE的高精度脑PET图像重建复合系统建模
在许多PET脑成像应用中,需要对大脑的特定区域进行高分辨率和良好的定量。通过使用具有扫描仪采集过程精确模型的统计重建方法,可以改善空间分辨率和示踪剂摄取的量化。该模型由系统响应矩阵表示,需要包括所有导致重构图像退化的因素。蒙特卡罗模拟是模拟PET中复杂物理过程的最佳方法,但其计算成本极高,并且每次新扫描都需要重新计算系统矩阵。此外,对于三维PET,系统矩阵可能有数十亿个元素,因此目前在迭代重建时无法存储在存储器中。因此,其他作者已经提出了系统矩阵的实时蒙特卡罗建模,其中蒙特卡罗模拟在正向投影仪中使用,而在反向投影仪中使用更简单的分析模型。在这项工作中,我们提出了一种不同的方法,其中使用复合系统矩阵,对视场的一小部分区域使用GATE计算完整的蒙特卡罗模型,并对该区域外的体素使用更简单的分析模型。我们使用纹状体幻像和脑幻像的二维模拟来评估该方法的可行性。对于每种情况,使用GATE生成以纹状体为中心的子区域的蒙特卡罗系统矩阵。采用所提出的方法重建脑模拟,并与临床使用的标准重建进行比较,有和没有分辨率建模。对于纹状体幻像,使用GATE系统矩阵显示重建图像的改进,其中更好地定义了纹状体区域的结构。对于使用复合系统矩阵的脑幻影,也观察到改善,但与纯GATE系统矩阵相比更有限。
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