Abstract
Optical molecular tomography (OMT) can monitor glioblastomas in small animals non-invasively. Although deep learning (DL) methods have made remarkable achievements in this field, improving its generalization against diverse reconstruction systems remains a formidable challenge. In this Letter, a free space matching network (FSMN-Net) was presented to overcome the parameter mismatch problem in different reconstruction systems. Specifically, a novel, to the best of our knowledge, manifold convolution operator was designed by considering the mathematical model of OMT as a space matching process. Based on the dynamic domain expansion concept, an end-to-end fully convolutional codec further integrates this operator to realize robust reconstruction with voxel-level accuracy. The results of numerical simulations and in vivo experiments demonstrate that the FSMN-Net can stably generate high-resolution reconstruction volumetric images under different reconstruction systems.
© 2024 Optica Publishing Group
Full Article | PDF ArticleMore Like This
Yongzhou Hua, Yuxuan Jiang, Kaixian Liu, Qingming Luo, and Yong Deng
Opt. Lett. 47(10) 2538-2541 (2022)
Yulei Bai, Zhanhua Zhang, Zhaoshui He, Shengli Xie, and Bo Dong
Opt. Lett. 49(3) 438-441 (2024)
Shuangchen Li, Jingjing Yu, Xuelei He, Hongbo Guo, and Xiaowei He
Opt. Lett. 47(7) 1729-1732 (2022)