Accelerating vasculature imaging in tumor using mesoscopic fluorescence molecular tomography via a hybrid reconstruction strategy

Mesoscopic fluorescent molecular tomography (MFMT) enables to image fluorescent molecular probes beyond the typical depth limits of microscopic imaging and with enhanced resolution compared to macroscopic imaging. However, MFMT is a scattering-based inverse problem that is an ill-posed inverse problem and hence, requires relative complex iterative solvers coupled with regularization strategies. Inspired by the potential of deep learning in performing image formation tasks from raw measurements, this work proposes a hybrid approach to solve the MFMT inverse problem. This methodology combines a convolutional symmetric network and a conventional iterative algorithm to accelerate the reconstruction procedure. By the proposed deep neural network, the principal components of the sensitivity matrix are extracted and the accompanying noise in measurements is suppressed, which helps to accelerate the reconstruction and improve the accuracy of results. We apply the proposed method to reconstruct in silico and vascular tree models. The results demonstrate that reconstruction accuracy and speed are highly improved due to the reduction of redundant entries of the sensitivity matrix and noise suppression. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

Mesoscopic fluorescent molecular tomography (MFMT) is a method for imaging fluorescent molecular probes beyond the depth limits of microscopic imaging. This paper proposes a hybrid approach combining a convolutional symmetric network and a conventional iterative algorithm to accelerate the reconstruction procedure and improve accuracy. The results show that reconstruction accuracy and speed are significantly improved using this method.