Brief review on learning-based methods for optical tomography

作     者：Lin Zhang Guanglei Zhang Lin Zhang;Guanglei Zhang

作者机构：Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing 100191P.R.China School of Biological Science and Medical Engineering Beihang University Beijing 100191P.R.China 

出 版 物：《Journal of Innovative Optical Health Sciences》 (创新光学健康科学杂志（英文）)

年 卷 期：2019年第12卷第6期

页      面：11-24页

核心收录：

学科分类：070207[理学-光学] 07[理学] 08[工学] 0803[工学-光学工程] 0702[理学-物理学] 

基　　金：supported by the Fundamental Research Funds for Central Universities,the National Natural Science Foundation of China(No.61601019,61871022) the 111 Project(No.B13003) 

主　　题：Optical imaging tomography inverse problem machine learning deep learning 

摘      要：Learning-based methods have been proved to perform well in a variety of areas in the biomedical field,such as biomedical image segmentation,and histopathological image *** learning,as the most recently presented approach of learning-based methods,has attracted more and more *** instance,massive researches of deep learning methods for image reconstructions of computed tomography(CT)and magnetic resonance imaging(MRI)have been reported,indicating the great potential of deep learning for inverse *** technology-related medical imaging modalities including diffuse optical tomography(DOT),fluorescence molecular tomography(FMT),bioluminescence tomography(BLT),and photo-acoustic tomography(PAT)are also dramatically innovated by introducing learning-based methods,in particular deep learning methods,to obtain better reconstruction *** review depicts the latest researches on learning based optical tomography of DOT,FMT,BLT,and *** to the most recent studies,learning-based methods applied in the field of optical tomography are categorized as kernel-based methods and deep learning *** this review,the former are regarded as a sort of conventional learning-based methods and the latter are subdivided into model-based methods,post-processing methods,and end-to-end *** as well as data acquisition strategy are discussed in this *** evaluations of these methods are summarized to ilustrate the performance of deep learning-based reconstruction.