2021-04-16T09:32:23Zhttps://repo.qst.go.jp/?action=repository_oaipmhoai:repo.qst.go.jp:000641902019-02-24T23:35:27Z00010:00029
GPU-based image reconstruction method including geometrical detector response functions for OpenPETenghttp://id.nii.ac.jp/1657/00064178/PresentationKinouchi, ShokoYamaya, TaigaYoshida, EijiTashima, HideakiKudou, HiroyukiSuga, MikioThe OpenPET, which has a physical gap between two detector rings, is our new geometry. In order to realize future radiation therapy guided by OpenPET, real-time imaging is required. Therefore we developed a list-mode image reconstruction method using general purpose graphic processing units (GPUs). For GPU implementation, the efficiency of acceleration depends on the implementation method which is required to avoid conditional statements. In this paper, therefore, we developed a new system model suitable for GPU implementation. In the proposed system model, each element of system matrix was calculated as the value of detector response function (DRF) of the length between the center of a voxel and a line of response (LOR). The DRF, which was calculated analytically to represent the probability distribution of each LOR, was modeled by a sixth-order polynomial function. The system model enabled us to calculate each element of the system matrix with reduced number of the conditional statements. We used the list-mode dynamic row-action maximum likelihood algorithm (DRAMA) which could reduce the number of iterations to only one. We applied the developed method to a small OpenPET prototype, which was developed for a proof-of-concept. The results showed that high quality reconstructed images were obtained using the proposed system model with 14.8 times faster than using the conventional system model.International Forum on Medical Imaging in Asia2011-01-19none2019-02-20