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 self-prior 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 self-prior 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.
Highlights
Fluorescence molecular tomography (FMT) visualizes the physiological and pathological process in biological tissues at cellular and molecular levels, through noninvasively monitoring the three-dimensional (3-D) distribution of fluorescent probes in the tissues in vivo
The strategy is designed as an iterative architecture and each iteration includes three steps: extraction of the self-prior information, modification of the cost function of the inverse problem and reconstruction of the distribution of fluorescence yield
To eliminate the influence of other factors, the number of iterations is set to be 5 and zeromean Gaussian noise with the signal-to-noise ratio (SNR) of 26 dB is added to the fluorescence data
Summary
Fluorescence molecular tomography (FMT) visualizes the physiological and pathological process in biological tissues at cellular and molecular levels, through noninvasively monitoring the three-dimensional (3-D) distribution of fluorescent probes in the tissues in vivo. Fluorescent probes are emitted by an excitation source and the fluorescence signals are detected in multiple angles. Spatial and quantitative distribution of fluorescent probes in the tissues can be obtained by tomographic reconstruction. In the studies of tumor detection, early-stage tumors are generally selected as the studied targets and labeled with specific fluorescent probes. In this situation the imaged targets are usually much smaller than the background in the image domain. The reconstruction task can be treated as a sparse problem. A series of L1-based reconstruction algorithms such as greedy reconstruction, restarted nonlinear conjugate gradient based L1 regularization, adaptive support driven reweighted L1-minimization and discrete cosine transform-based regularization are proposed to solve this problem [6,7,8,9,10]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
