A GPU-accelerated fully 3D OSEM image reconstruction for a high-resolution small animal PET scanner using dual-ended readout detectors. (2nd December 2020)

• Record Type: Journal Article
• Authors: Zeng, Tianyi; Gao, Juan; Gao, Dongfang; Kuang, Zhonghua; Sang, Ziru; Wang, Xiaohui; Hu, Lingzhi; Chen, Qun; Chu, Xu; Liang, Dong; Liu, Xin; Yang, Yongfeng; Zheng, Hairong; Hu, Zhanli
• Is Part Of: Physics in medicine & biology. Volume 65:Number 24(2020:Dec.)
• Publication Date: 2020-12-02
• Subjects: small animal PET -- depth of interaction (DOI) -- GPU-accelerated image reconstruction -- point spread function (PSF); Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aba6f9
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: In this work, a GPU-accelerated fully 3D ordered-subset expectation maximization (OSEM) image reconstruction with point spread function (PSF) modeling was developed for a small animal PET scanner with a long axial field of view (FOV). Dual-ended readout detectors that provided high depth of interaction (DOI) resolution were used for the small animal PET scanner to simultaneously achieve uniform high spatial resolution and high sensitivity. First, we developed a novel sinogram generation method, in which the dimension of the sinogram was determined first and then an event was assigned to a few neighboring sinogram elements by using weights that are inversely proportional to the distance from the measured line of response (LOR) to the LOR of the sinogram elements. System geometric symmetry, precomputation of LOR-driven ray-tracing and texture memory were applied to accelerate the GPU-based reconstruction. We developed a spatially variant PSF model where the PSF parameters were obtained by using point source images measured at 18 positions in the FOV and a spatial invariant PSF model where the PSF parameters were obtained by using only one image measured at the center FOV. The performance of the image reconstruction method was evaluated by using simulated phantom data as well as phantom and in-vivo mouse data acquired on the scanner. The results showed that the proposed reconstruction method provided better spatial resolution, a higher contrast recovery coefficient and lower noise than the OSEM reconstruction and was more than 1000 times faster than the CPU-based reconstruction. The spatially variant PSF model did not result in any spatial resolution improvement compared to the spatial invariant PSF model, and thus, the latter that is much easier to implement in image reconstruction and can be used in a small animal PET scanner using detectors with very high DOI resolution. A whole body 18 F-FDG mouse image with high resolution and a high contrast to noise ratio was obtained by using the proposed reconstruction method.