Compton camera has great potential in nuclear medicine imaging, and the pre-calculation and evaluation of the point spread function (PSF) and detection efficiency can provide important help for the optimization of the Compton camera systems and the improvement of image reconstruction quality. In this paper, we propose a method for calculating the spatial PSF and detection efficiency of the Compton camera system based on differential transmission probability and GPU acceleration. Different from the existing assumptions based on the uniform distribution of photon action positions in the detector, we consider the changes in the transmission probability at different action positions to obtain a more accurate calculation of the spatial point spread function. Besides, we combine the advantages of GPU parallel computing to divide the calculation of transmission probability into subsets of differential voxels for parallel computing, which greatly improves the calculation speed. The proposed approach could complete the calculation of the detection efficiency in the region of interest of the Compton camera system in about 10 seconds and complete the accurate evaluation of the Compton camera system's spatial PSF within half an hour, with the deviation of less than 1 mm. The rapid estimation of the PSF and the detection efficiency of the Compton camera system obtained by the proposed method could provide the performance estimation and help the optimization of the Compton camera design, and can also improve the accuracy of image reconstruction by using the PSF.
KEYWORDS: Head, Monte Carlo methods, In vivo imaging, Cameras, Reconstruction algorithms, Device simulation, Computer simulations, Optical simulations, Gamma ray imaging
Prompt gamma ray (PG) imaging based on Compton camera (CC) has been proposed to realize in vivo verification during the proton therapy. However, due to the inherent geometrical complexity of Compton camera data, PG imaging can be time-consuming and difficult to reconstruct in real-time, while using standard techniques such as filtered back-projection (FBP) or list-mode maximum likelihood-expectation maximization (LM-MLEM). In addition, the imaging quality and spatial resolution of the reconstructed PG images is seriously limited by the finite energy and spatial resolution of CC, as well as the Doppler broaden effect. In this paper, we investigate the performance of in vivo verification via PG imaging with a three-stage Cadmium Zinc Telluride (CZT) pixelated Compton camera during the proton therapy for human head. We demonstrated the real-time PG imaging approach by using Monte Carlo back-projection (MC-BP) and triple events. The prompt gammas were induced by a 69MeV ~ 86 MeV proton pencil beam irradiating the human head phantom, which were simulated by using Geant4 toolkit. The results show that the reconstructions with Compton camera imaging realized nearly real-time PG imaging with a good resolution recovery, as well as provided the accurate estimation of in-vivo verification, thus demonstrating the feasibility in PG-based in-vivo proton range verification with CC.
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