论文标题
ParallelProj-用于快速计算层析成像预测的开源框架
PARALLELPROJ -- An open-source framework for fast calculation of projections in tomography
论文作者
论文摘要
在本文中，我们介绍了一个新型的开源框架，旨在有效地平行层析成像的投影并行计算，以利用多个CPU核心或GPU。该框架利用约瑟夫的方法有效地实现了正式和列表模式数据的前向和向后投影功能，该方法进一步扩展到包含飞行时间（TOF）PET的投影。我们的评估涉及一系列测试，重点是使用来自最先进的临床PET/CT系统的数据进行PET图像重建。我们在非TOF和TOF，Sinogram和ListMode中彻底基准了投影仪的性能，并采用了多核核，混合CPU/GPU和独家GPU模式。此外，我们还调查了在搅拌中计算出的非TOF Sinogram投影的时机（用于断层图像重建的软件），该预测最近将ParallelProj作为其投影后端之一。我们的结果表明，相对于多CPU核模式，独家GPU模式提供了25至68之间的加速因子。此外，我们证明了使用单个消费者GPU在几秒钟内可以实现最新的现实世界宠物数据集的OSEM ListMode重建。
In this article, we introduce, a novel open-source framework designed for efficient parallel computation of projections in tomography leveraging either multiple CPU cores or GPUs. This framework efficiently implements forward and back projection functions for both sinogram and listmode data, utilizing Joseph's method, which is further extended to encompass time-of-flight (TOF) PET projections. Our evaluation involves a series of tests focusing on PET image reconstruction using data sourced from a state-of-the-art clinical PET/CT system. We thoroughly benchmark the performance of the projectors in non-TOF and TOF, sinogram, and listmode employing multi CPU-cores, hybrid CPU/GPU, and exclusive GPU mode. Moreover, we also investigate the timing of non-TOF sinogram projections calculated in STIR (Software for Tomographic Image Reconstruction) which recently integrated parallelproj as one of its projection backends. Our results indicate that the exclusive GPU mode provides acceleration factors between 25 and 68 relative to the multi-CPU-core mode. Furthermore, we demonstrate that OSEM listmode reconstruction of state-of-the-art real-world PET data sets is achievable within a few seconds using a single consumer GPU.
