Towards Software-Based Real-Time Singles and Coincidence Processing of Digital PET Detector Raw Data
Towards Software-Based Real-Time Singles and Coincidence Processing of Digital PET Detector Raw Data
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DOI：
10.1109/tns.2013.2252193
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发表时间：
2013-06-01
影响因子：
1.8
通讯作者：
Schulz, Volkmar
中科院分区：
文献类型：
--
作者：
Goldschmidt, Benjamin;Lerche, Christoph W.;Schulz, Volkmar
本文提出了一种基于软件的单一和符合处理（SCP）架构的数字PET/MR系统，是基于SiPM探测器与本地数字化耦合到临床前晶体阵列。与传统的PET系统相比，我们的系统输出探测器的原始数据的单个探测器元件通过光千兆以太网接口，而不是单一或巧合。原始数据包含触发的SiPM像素（命中）的数字化时间戳、能量和标识符。虽然这种方法需要探测器数据传输系统的高带宽，但探测器原始数据的可用性提供了采用更准确和计算复杂的迭代算法的独特机会，这可以产生具有更高质量和准确度的PET图像。在本文中，我们评估了三种不同的晶体位置估计方法，其实时能力的并行软件为基础的SCP。SCP接收探测器原始数据作为输入，并输出列表模式符合数据。所研究的PET系统具有10个单处理单元（SPU），每个单元配备两个PET探测器堆栈和一个千兆以太网接口，连接到数据采集和处理服务器（Dell Poweredge R910，配备4个Intel Xeon X7560@2.27 GHz CPU和256 GByte DDR3-RAM），允许无损实时采集整个原始数据流。使用之前存储的三个测量值的检测器原始数据，我们的结果表明，基于重心（COG）的并行SCP的吞吐量（以兆次/秒为单位）平均比估计的检测器原始数据输出高出近13倍，该输出是由37 MBq的活动生成的。在检测器环的等中心。在相同的条件下，基于迭代最大似然（ML）的并行SCP导致平均6倍的吞吐量，而基于高斯的并行SCP也导致平均13倍的吞吐量。与串行处理方法相比，并行实现对于基于ML的平均加速高达38倍，对于基于高斯的平均加速高达39倍，对于基于COG的并行化SCP对于三个先前存储的测量平均加速高达34倍。
This paper presents a software-based singles and coincidence processing (SCP) architecture for a digital PET/MR system that is based on SiPM detectors with local digitization coupled to preclinical crystal arrays. Compared with traditional PET systems, our system outputs detector raw data of the individual detector elements via optical Gigabit Ethernet interfaces instead of singles or coincidences. The raw data contains the digitized timestamps, energies, and identifiers of triggered SiPM pixels (hits). Although this approach requires a high bandwidth for the detector data transmission system, the availability of detector raw data offers unique opportunities to employ more accurate and computationally complex, iterative algorithms, which can lead to PET images with higher quality and accuracy. In this paper, we evaluate a parallel software-based SCP for three different crystal position estimation approaches with regard to its real-time capabilities. The SCP receives detector raw data as input and outputs list-mode coincidence data. The investigated PET system features ten singles processing units (SPU), each equipped with two PET detector stacks and a Gigabit Ethernet interface to a data acquisition and processing server (Dell Poweredge R910 equipped with 4x Intel Xeon X7560@2.27 GHz CPUs and 256 GByte DDR3-RAM), allowing lossless real-time acquisition of the entire raw data stream. Using the detector raw data of three previously stored measurements, our results show that the throughput (in Mhits/s) of a center-of-gravity (COG)-based parallel SCP is nearly 13x higher on average than the estimated detector raw data output that is generated from an activity of 37 MBq in the iso-center of the detector ring. Under the same conditions, an iterative maximum-likelihood (ML)-based parallel SCP leads to a 6x higher throughput on average, while a Gaussian-based parallel SCP also results in a 13x higher throughput on average. Compared with a serial processing approach, the parallel implementations show speedups of up to 38x on average for the ML-based, 39x on average for Gaussian-based, and up to 34x on average for the COG-based parallelized SCP for the three previously-stored measurements.