3D PET Reconstruction Using Generalized Natural Pixels

描述(由申请人提供)：在过去的十年中，临床PET在肿瘤成像应用中的使用大幅增加，这主要是由于18F-FDG的可用性增加所致。为了最大限度地提高灵敏度，PET扫描仪设计的最新趋势是更快、更明亮的闪烁体、更大的轴向尺寸，以及不使用隔板获取全三维(3D)数据。这些趋势导致重建进程更加复杂。这项建议的总体目标是为PET开发3D图像重建方法，以提供更高的诊断准确性。临床上最常用的三维PET重建方法是结合二维重建的再分组法。虽然这种方法可以以较低的计算成本实现，但它将数据建模为通过对象的线积分，因此不能准确地解释PET中探测器的空间变化响应。实际上，成像过程中有许多物理效应使这一线积分假设失效。这些影响包括：正电子射程、非共线性、空间变化的几何效率、晶体间穿透、晶体散射以及在探测器块内准确定位相互作用位置的不确定性。在这个项目中，我们建议开发和研究一种3DPET方法，它使用替代的基函数(相对于体素基函数)来描述感兴趣的对象。这些基函数充分利用了PET几何结构中存在的对称性，从而产生了具有块循环性质的系统响应矩阵。这些特性使得能够在存储器中存储整个3D系统响应矩阵的情况下实现重建算法，并且具有非常快的计算时间。使用精确的蒙特卡罗模拟程序(GATE)计算三维系统响应矩阵。将利用临床图像进行心理物理观察者研究，以评估不同重建方法在1厘米以下肿瘤检测方面的改善。如果拟议的重建方法在意图上成功，对疑似或已知癌症患者的护理将得到改善。与公共卫生相关：这项提案的总体目标是为PET开发3D图像重建方法，以提供更高的诊断准确性。将利用临床图像进行心理物理观察者研究，以评估不同重建方法在1厘米以下肿瘤检测方面的改善。如果拟议的重建方法在意图上成功，对疑似或已知癌症患者的护理将得到改善。

DESCRIPTION (provided by applicant): In the past ten years, there has been a substantial increase in the use of clinical PET for oncological imaging applications, which has primarily been driven by the increased availability of 18F-FDG. In order to maximize sensitivity, the recent trend in PET scanner design is for faster and brighter scintillators, larger axial dimensions, and acquisition of fully three-dimensional (3D) data, without the use of septa. These trends result in an increased complexity of the reconstruction process. The overall goal of this proposal is to develop 3D image reconstruction methods for PET that can provide improved diagnostic accuracy. The most common approach for clinical 3D PET reconstruction is to use a re-binning method combined with 2D reconstruction. While this approach can be implemented with low computational cost, it models the data as line integrals through the object, and thus cannot accurately account for the spatially variant detector response in PET. In actuality, there are a number of physical effects in the imaging process that invalidate this line integral assumption. These include such effects as: positron range, non-collinearity, spatially variant geometric efficiency, inter-crystal penetration, crystal scatter, and uncertainties in accurately locating the position of interaction within the detector block. In this project, we propose to develop and investigate an approach for 3D PET that use alternative basis functions (as opposed to voxel basis functions) to describe the object of interest. These basis functions takes full advantage of the symmetries present in the PET geometry resulting in a system response matrix with block circulant properties. These properties make it possible to implement the reconstruction algorithm with storage of the entire 3D system response matrix in memory, and with very fast computation time. An accurate Monte Carlo simulation code (GATE) will be used to compute the 3D system response matrix. Psychophysical observer studies, using clinical images, will be conducted to evaluate improvements in sub 1 cm tumor detection with different reconstruction methods. If the proposed reconstruction methods are successful in their intent, the care of patients with suspected or known cancer will be improved. PUBLIC HEALTH RELEVANCE: The overall goal of this proposal is to develop 3D image reconstruction methods for PET that can provide improved diagnostic accuracy. Psychophysical observer studies, using clinical images, will be conducted to evaluate improvements in sub 1 cm tumor detection with different reconstruction methods. If the proposed reconstruction methods are successful in their intent, the care of patients with suspected or known cancer will be improved.