Gates joint locally connected network for accurate and robust reconstruction in optical molecular tomography
作者机构：School of Computer Science and EngineeringXi’an University of TechnologyXi’anChina School of Information Science and TechnologyNorthwest UniversityXi’an 710127China
出 版 物：《Journal of Innovative Optical Health Sciences》 (创新光学健康科学杂志（英文）)
年 卷 期：2024年第17卷第3期
页 面：11-22页
核心收录：
学科分类：070207[理学-光学] 07[理学] 08[工学] 1010[医学-医学技术（可授医学、理学学位）] 0803[工学-光学工程] 0702[理学-物理学]
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)
主 题：Optical molecular tomography gates module positioning accuracy robustness
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 *** 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 ***,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 ***,gates module was composed of the concatenation and multiplication operators of three different *** 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 *** evaluate the performance of the proposed method,numerical simulations were conducted,whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.
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