Cao X, Li K, Xu XL, Deneen KMV, Geng GH, Chen XL. Development of tomographic reconstruction for three-dimensional optical imaging: From the inversion of light propagation to artificial intelligence. Artif Intell Med Imaging 2020; 1(2): 78-86 [DOI: 10.35711/aimi.v1.i2.78]
Corresponding Author of This Article
Xue-Li Chen, PhD, Professor, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, and School of Life Science and Technology, Xidian University, No. 266 Xinglong Section of Xifeng Road, Xi’an 710126, Shaanxi Province, China. xlchen@xidian.edu.cn
Research Domain of This Article
Engineering, Biomedical
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Med Imaging. Aug 28, 2020; 1(2): 78-86 Published online Aug 28, 2020. doi: 10.35711/aimi.v1.i2.78
Development of tomographic reconstruction for three-dimensional optical imaging: From the inversion of light propagation to artificial intelligence
Xin Cao, Kang Li, Xue-Li Xu, Karen M von Deneen, Guo-Hua Geng, Xue-Li Chen
Xin Cao, Kang Li, Xue-Li Xu, Guo-Hua Geng, School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
Karen M von Deneen, Xue-Li Chen, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, and School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
Author contributions: Cao X performed the majority of the writing and the investigation of articles; Li K and Xu XL performed the literature search and writing for the light propagation model-based OMT algorithm; Geng GH performed writing for the machine learning-based OMT algorithm; von Deneen KM polished the language and expression of the paper; Chen XL checked the organization and revised the writing of the paper.
Supported bythe National Natural Science Foundation of China, No. 61701403; the Project Funded by China Post-doctoral Science Foundation, No. 2018M643719; the Young Talent Support Program of the Shaanxi Association for Science and Technology, No. 20190107; the Scientific Research Program Funded by Shaanxi Provincial Education Department, No. 18JK0767; and the Natural Science Research Plan Program in Shaanxi Province of China, No. 2017JQ6006.
Conflict-of-interest statement: The authors confirm having no conflict of interest in relation to this article’s content.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Xue-Li Chen, PhD, Professor, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, and School of Life Science and Technology, Xidian University, No. 266 Xinglong Section of Xifeng Road, Xi’an 710126, Shaanxi Province, China. xlchen@xidian.edu.cn
Received: June 5, 2020 Peer-review started: June 5, 2020 First decision: June 4, 2020 Revised: August 1, 2020 Accepted: August 22, 2020 Article in press: August 22, 2020 Published online: August 28, 2020 Processing time: 94 Days and 0.9 Hours
Abstract
Optical molecular tomography (OMT) is an imaging modality which uses an optical signal, especially near-infrared light, to reconstruct the three-dimensional information of the light source in biological tissue. With the advantages of being low-cost, noninvasive and having high sensitivity, OMT has been applied in preclinical and clinical research. However, due to its serious ill-posedness and ill-condition, the solution of OMT requires heavy data analysis and the reconstruction quality is limited. Recently, the artificial intelligence (commonly known as AI)-based methods have been proposed to provide a different tool to solve the OMT problem. In this paper, we review the progress on OMT algorithms, from conventional methods to AI-based methods, and we also give a prospective towards future developments in this domain.
Core Tip: Most of the existing review articles about optical molecular tomography (OMT) focus on the traditional light propagation model-based algorithm, which possesses ill-posedness and ill-condition and the reconstruction result is unsatisfactory. The emergence of deep learning has brought OMT into the era of artificial intelligence, which can obtain a highly accurate reconstruction result. This article systematically reviews the development of tomographic reconstruction for OMT, which involves the light propagation model-based OMT algorithm and machine learning-based OMT algorithm. The challenges and perspectives of these machine learning-based algorithms are given at the end of the article.
