Gates joint locally connected network for accurate and robust reconstruction in optical molecular tomography  

作　　者：Minghua Zhao Yahui Xiao Jiaqi Zhang Xin Cao Lin Wang 

机构地区：[1]School of Computer Science and Engineering,Xi’an University of Technology,Xi’an,China [2]School of Information Science and Technology,Northwest University,Xi’an 710127,China

出　　处：《Journal of Innovative Optical Health Sciences》2024年第3期11-22,共12页创新光学健康科学杂志（英文）

基　　金：supported by the National Natural Science Foundation of China(No.62101439);the Key Research and Development Program of Shaanxi(No.2023-YBSF-289).

摘　　要：Optical molecular tomography(OMT)is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas,which can provide non-invasive quantitative three-dimensional(3D)information regarding tumor distribution in living animals.The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results,resulting in problems such as low accuracy,poor robustness,and long-time consumption.Here,a gates joint locally connected network(GLCN)method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly,thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy.Moreover,gates module was composed of the concatenation and multiplication operators of three different gates.It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates.To evaluate the performance of the proposed method,numerical simulations were conducted,whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.

关 键 词：Optical molecular tomography gates module positioning accuracy ROBUSTNESS 

分 类 号：O43[机械工程—光学工程]