GCR-Net:3D Graph convolution-based residual network for robust reconstruction in cerenkov luminescence tomography  

作　　者：Weitong Li Mengfei Du Yi Chen Haolin Wang Linzhi Su Huangjian Yi Fengjun Zhao Kang Li Lin Wang Xin Cao 

机构地区：[1]School of Information Science and Technology Northwest University,Xi'an,Shaanxi 710127,P.R.China [2]National and Local Joint Engineering Research Center for Cultural Heritage Digitization,Xi'an,Shaanxi 710127,P.R.China [3]Xi'an University of Technology Xi'an,Shaanxi 710127,P.R.China

出　　处：《Journal of Innovative Optical Health Sciences》2023年第1期15-25,共11页创新光学健康科学杂志（英文）

基　　金：National Key Research and Development Program of China (2019YFC1521102);National Natural Science Foundation of China (61701403,61806164,62101439,61906154);China Postdoctoral Science Foundation (2018M643719);Natural Science Foundation of Shaanxi Province (2020JQ-601);Young Talent Support Program of the Shaanxi Association for Science and Technology (20190107);Key Research and Development Program of Shaanxi Province (2019GY-215,2021ZDLSF06-04);Major research and development project of Qinghai (2020-SF-143).

摘　　要：Cerenkov Luminescence Tomography(CLT)is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes.However,due to severe ill-posed inverse problem,obtaining accurate reconstruction results is still a challenge for traditional model-based methods.The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source,which effectively improves the performance of CLT reconstruction.However,the previously proposed deep learning-based methods cannot work well when the order of input is disarranged.In this paper,a novel 3D graph convolution-based residual network,GCR-Net,is proposed,which can obtain a robust and accurate reconstruction result from the photon intensity of the surface.Additionally,it is proved that the network is insensitive to the order of input.The performance of this method was evaluated with numerical simulations and in vivo experiments.The results demonstrated that compared with the existing methods,the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing threedimensional information.

关 键 词：Cerenkov luminescence tomography optical molecular imaging optical tomography deep learning 3D graph convolution 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]