A GPU-accelerated fully 3D OSEM image reconstruction for a high-resolution small animal PET scanner using dual-ended readout detectors

Author:

Zeng Tianyi,Gao Juan,Gao Dongfang,Kuang ZhonghuaORCID,Sang Ziru,Wang Xiaohui,Hu Lingzhi,Chen Qun,Chu Xu,Liang DongORCID,Liu Xin,Yang Yongfeng,Zheng Hairong,Hu ZhanliORCID

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 18F-FDG mouse image with high resolution and a high contrast to noise ratio was obtained by using the proposed reconstruction method.

Funder

National Natural Science Foundation of China

Publisher

IOP Publishing

Subject

Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

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1. PARALLELPROJ—an open-source framework for fast calculation of projections in tomography;Frontiers in Nuclear Medicine;2024-01-08

2. Deep Generalized Learning Model for PET Image Reconstruction;IEEE Transactions on Medical Imaging;2024-01

3. Effect of depth of interaction resolution on the spatial resolution of SIAT aPET;Physics in Medicine & Biology;2023-11-10

4. Image Reconstruction with DOI Rebinning and PSF Modeling for the Quad-Head PET;2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD);2023-11-04

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