Optical molecular imaging is an emerging medical imaging technology that integrates mathematics, chemistry, biology, computer science, optics and many other disciplines. This imaging technology enables in vivo level dynamic imaging of physiological processes at the tissue level and even at the cellular level. Due to the advantages of high spatial resolution, high detection sensitivity and good safety, optical molecular imaging has been developed rapidly since its introduction and is widely used in many fields such as surgical navigation, drug development and drug efficacy assessment.
Fluorescence molecular tomography (FMT) is an important imaging modality in optical molecular imaging techniques. This imaging technique uses fluorescence probes for surface fluorescence imaging of tumour-specific target molecular markers, and then uses reconstruction algorithms combined with tomography for three-dimensional reconstruction to achieve a three-dimensional dynamic observation of fluorescence probe distribution in vivo. This imaging technique improves the reconstruction accuracy of FMT by incorporating tomographic techniques compared to two- dimensional fluorescence molecular imaging. Traditional FMT reconstruction methods rely on a mathematical model of photon propagation in an organism, mainly through a first-order spherical harmonic simplification of the radiative transfer equation to obtain the system matrix of photon propagation, and then solve for the in-organism light source based on the system matrix and the surface fluorescence photon density distribution vector. However, there is error in obtaining the system matrix through the spherical harmonic approximation, which ultimately affects the accuracy of the FMT reconstruction. In addition, traditional reconstruction methods mainly use coordinate descent methods based on a priori of regularization to solve, and the results generally show problems as over-sparse or over-smooth, spatial discontinuity and low accuracy of morphological reconstruction. Therefore, this work addresses the above problems and conducts a study of graph network-based algorithms for FMT reconstruction of glioblastoma. At the same time, based on the priori information of the spatial structure of glioblastoma, which are often distributed in clusters in organisms, the graph network algorithm is used to extract the spatial structure information of organisms to optimize the final reconstruction results of FMT reconstruction with hig localization accuracy, low morphological error, improved robustness and other aspects. The aim is to optimize the performance of the reconstruction algorithm in terms of localization accuracy, morphological error, robustness, etc. The main research content and innovative contributions of this work are summarised as follows.
1. FMT reconstruction algorithm based on Graph Convolutional residual connection network
In cancer patients, the tumours tends to show a cluster-like distribution, therefore, fluorescence probes targeting specific tumours also have a priori information about the structure of cluster-like distribution in patients. Based on this priori information, this work proposes a FMT reconstruction algorithm based on a graph convolutional network residual connection network. An important innovation of the algorithm is its use of a fully connected network to fit the nonlinear mapping relationship between the distribution of surface fluorescence and the distribution of fluorescence probes in vivo. In addition, it also uses a graph convolutional network to fuse the characteristics of adjacent nodes in the graph to morphologically optimize the preliminary localization results and improve the overall morphological reconstruction accuracy. To verify the reconstruction performance of the algorithm, single-source as well as large-source sample quantitative experiments, dual-light reconstruction experiments of digital mice and real glioma bearing mice experiments were designed for the test set. The experimental results show that the algorithm significantly improves the morphological reconstruction accuracy of 3D reconstruction based on the IPS algorithm.
2. FMT reconstruction algorithm based on the Graph-Unet residual connectivity network
In the traditional FMT reconstruction algorithm, the use of tomographic structure information is only limited to tissue dissection and tetrahedral mesh generation, but the structural information is not used to improve the accuracy of FMT reconstruction. In order to make full use of the anatomical structure information, this work proposes a FMT reconstruction algorithm based on Graph-Unet residual connectivity network. The encoder-decoder structure of Graph-Unet is used to extract the anatomical structure information at different scales, and the attention-based graph attention network is used to fuse the surface fluorescence features with the anatomical structure features to improve the accuracy of the FMT reconstruction. For this algorithm, single-source reconstruction experiments with different noise levels, double-source reconstruction experiments with different spacing and complex large-source reconstruction experiments with different depths were designed based on glioma digital mice, and real glioma mice in vivo experiments were also conducted. The experimental results show that this method can effectively alleviate the degradation of morphological reconstruction performance caused by over-sparse and over-smooth reconstruction compared with the comparative method, and thus improve the accuracy of its 3D morphological reconstruction.