Département de radio-oncologie, Centre hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada.
Phys Med Biol. 2012 Oct 7;57(19):6279-93. doi: 10.1088/0031-9155/57/19/6279. Epub 2012 Sep 14.
During the last decade, studies have shown that 3D list-mode ordered-subset expectation-maximization (LM-OSEM) algorithms for positron emission tomography (PET) reconstruction could be effectively computed and considerably accelerated by graphics processing unit (GPU) devices. However, most of these studies rely on pre-calculated sensitivity matrices. In many cases, the time required to compute this matrix can be longer than the reconstruction time itself. In fact, the relatively long time required for the calculation of the patient-specific sensitivity matrix is considered as one of the main obstacle in introducing a list-mode PET reconstruction algorithm for routine clinical use. The objective of this work is to accelerate a fully 3D LM-OSEM algorithm, including the calculation of the sensitivity matrix that accounts for the patient-specific attenuation and normalization corrections. For this purpose, sensitivity matrix calculations and list-mode OSEM reconstructions were implemented on GPUs, using the geometry of a commercial PET system. The system matrices were built on-the-fly by using an approach with multiple rays per detector pair. The reconstructions were performed for a volume of 188 × 188 × 57 voxels of 2 × 2 × 3.15 mm(3) and for another volume of 144 × 144 × 57 voxels of 4 × 4 × 3.15 mm(3). The time to compute the sensitivity matrix for the 188 × 188 × 57 array was 9 s while the LM-OSEM algorithm performed at a rate of 1.1 millions of events per second. For the 144 × 144 × 57 array, the respective numbers are 8 s for the sensitivity matrix and 0.8 million of events per second for the LM-OSEM step. This work lets envision fast reconstructions for advanced PET applications such as real time dynamic studies and parametric image reconstructions.
在过去的十年中，研究表明，用于正电子发射断层扫描（PET）重建的 3D 列表模式有序子集期望最大化（LM-OSEM）算法可以通过图形处理单元（GPU）设备有效地计算和显著加速。然而，这些研究中的大多数都依赖于预计算的灵敏度矩阵。在许多情况下，计算此矩阵所需的时间可能比重建本身所需的时间更长。实际上，计算患者特定灵敏度矩阵所需的相对较长的时间被认为是引入用于常规临床使用的列表模式 PET 重建算法的主要障碍之一。本工作的目的是加速完全 3D LM-OSEM 算法，包括计算考虑患者特定衰减和归一化校正的灵敏度矩阵。为此，使用商业 PET 系统的几何形状，在 GPU 上实现了灵敏度矩阵计算和列表模式 OSEM 重建。通过使用每对探测器多条射线的方法在线构建系统矩阵。对 2×2×3.15mm(3)的 188×188×57 体素和 4×4×3.15mm(3)的 144×144×57 体素的体积进行了重建。计算 188×188×57 阵列灵敏度矩阵的时间为 9 秒，而 LM-OSEM 算法的运行速度为每秒 110 万个事件。对于 144×144×57 阵列，灵敏度矩阵的相应数字分别为 8 秒和每秒 80 万个事件的 LM-OSEM 步骤。这项工作为实时动态研究和参数图像重建等高级 PET 应用的快速重建提供了可能。
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