Published online Aug 28, 2020. doi: 10.35711/aimi.v1.i2.78
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
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.