Alternating Direction Method of Multipliers Network for Bioluminescence Tomography Reconstruction

• Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2021; pp. 3109 - 3113
• Main Authors: Guo, Hongbo, Zhao, Hengna, Song, Xiaolei, He, Xiaowei
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.11.2021
• Subjects: Algorithms; Animals; Bioluminescence; Computer Simulation; Deep learning; Heuristic algorithms; In vivo; Molecular imaging; Tomography; Tomography, X-Ray Computed; Training data; Visualization
• ISSN: 2694-0604
• DOI: 10.1109/EMBC46164.2021.9630213

Bioluminescence tomography (BLT) is an effective noninvasive molecular imaging modality for three dimensional visualization of in vivo tumor research in small animals. The approaches of deep learning have shown great potential in the field of optical molecular imaging in recent years. However, the common problem with these existing end-to-end networks is the black box technology, whose solving process is not theoretically proven. In this work, we proposed a novel Alternating Direction Method of Multipliers Network (ADMM-Net) to solve the poor interpretation problem of internal process. The ADMM-Net combines the framework of deep learning on the basis of traditional ADMM algorithm to dynamically learn various parameters of the algorithm in the form of network. To evaluate the performance of our proposed network, we implemented numerical simulation experiments. The results show that the ADMM-Net can accurately reconstruct the location of the source, and the morphological similarity with the real source is also higher.