Neural-field-based image reconstruction for bioluminescence tomography  

作　　者：Xuanxuan Zhang Xu Cao Jiulou Zhang Lin Zhang Guanglei Zhang 

机构地区：[1]School of Communications and Information Engineering&School of Artificial Intelligence,Xi'an University of Posts and Telecommunications Xi'an,Shaanxi 710121,P.R.China [2]Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education&School of Life Science and Technology,Xidian University Xi'an,Shaanxi 710071,P.R.China [3]Department of Radiology,The First Afiliated Hospital of Nanjing Medical University Nanjing,Jiangsu 210029,P.R.China [4]School of Information Science and Engineering,Shandong Normal University Jinan,Shandong 250014,P.R.China [5]School of Biological Science and Medical Engineering,Beihang University Beijing 100191,P.R.China

出　　处：《Journal of Innovative Optical Health Sciences》2025年第1期165-179,共15页创新光学健康科学杂志(英文)

基　　金：supported in part by the National Natural Science Foundation of China(62101278,62001379,62271023);Beijing Natural Science Foundation(7242269).

摘　　要：Deep learning(DL)-based image reconstruction methods have garnered increasing interest in the last few years.Numerous studies demonstrate that DL-based reconstruction methods function admirably in optical tomographic imaging techniques,such as bioluminescence tomography(BLT).Nevertheless,nearly every existing DL-based method utilizes an explicit neural representation for the reconstruction problem,which either consumes much memory space or requires various complicated computations.In this paper,we present a neural field(NF)-based image reconstruction scheme for BLT that uses an implicit neural representation.The proposed NFbased method establishes a transformation between the coordinate of an arbitrary spatial point and the source value of the point with a relatively light-weight multilayer perceptron,which has remarkable computational efficiency.Another simple neural network composed of two fully connected layers and a 1D convolutional layer is used to generate the neural features.Results of simulations and experiments show that the proposed NF-based method has similar performance to the photon density complement network and the two-stage network,while consuming fewer floating point operations with fewer model parameters.

关 键 词：Bioluminescence tomography image reconstruction neural field 

分 类 号：R318[医药卫生—生物医学工程] TP391.41[医药卫生—基础医学]