Self-prior strategy for organ reconstruction in fluorescence molecular tomography

Abstract

Abstract:The purpose of this study is to propose a strategy for organ reconstruction in fluorescence molecular tomography (FMT) without prior information from other imaging modalities, and to overcome the high cost and ionizing radiation caused by the traditional structural prior strategy. The proposed strategy is designed as an iterative architecture to solve the inverse problem of FMT. In each iteration, a short time Fourier transform (STFT) based algorithm is used to extract the self-prior information in the space-frequency energy spectrum with the assumption that the regions with higher fluorescence concentration have larger energy intensity, then the cost function of the inverse problem is modified by the selfprior information, and lastly an iterative Laplacian regularization algorithm is conducted to solve the updated inverse problem and obtains the reconstruction results. Simulations and in vivo experiments on liver reconstruction are carried out to test the performance of the selfprior strategy on organ reconstruction. The organ reconstruction results obtained by the proposed self-prior strategy are closer to the ground truth than those obtained by the iterative Tikhonov regularization (ITKR) method (traditional non-prior strategy). Significant improvements are shown in the evaluation indexes of relative locational error (RLE), relative error (RE) and contrast-to-noise ratio (CNR). The self-prior strategy improves the organ reconstruction results compared with the non-prior strategy and also overcomes the shortcomings of the traditional structural prior strategy. Various applications such as metabolic imaging and pharmacokinetic study can be aided by this strategy.

Reconstruction of fluorescence molecular tomography based on graph convolution networks

“…To further explore and utilize the spatial information in the data, it is necessary to consider the spatial topological relationship of data itself. Some methods have been reported to introduce structural prior information into the reconstruction process of FMT by shaping a regularization matrix [15,16]. But these methods still rely on iteration and are hindered by the ill-posed nature of the inverse problem.…”

Recent methodology advances in fluorescence molecular tomography

“…A small signal disturbance may lead to a large reconstruction error. Therefore, researchers apply regularization techniques to FMT reconstruction to constrain the reconstruction process and reduce morbidity [8,9,28,30,63,[98][99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115]. The main principle of regularization is as follows:…”

Fluorescence Molecular Tomography Reconstruction of Small Targets Using Stacked Auto-Encoder Neural Networks

“…Fluorescence molecular tomography (FMT) has attracted widespread interest due to its non-invasive property and sensitivity in applications such as drug discovery [1], cancer detection [2], and gene expression visualization [3], among others. However, FMT reconstruction is highly illconditioned because of the lack of sufficient external measurements required for complex images [4]- [6].…”