Bioluminescence tomography (BLT) is an effective noninvasive molecular imaging modality, it has shown great potential for studying and monitoring disease progression in pre-clinical imaging. As the BLT is an inherent highly ill-posed inverse problem, it is still a challenge to obtain an accurate reconstruction result. Some algorithms have been proposed to solve highly ill posedness of inverse problems. Nevertheless, Existing methods always need to consume large time or have low interpretability. Thus, in this paper, we proposed a novel model-driven deep learning network, which unfolding the Fast Iterative Shrinkage Thresholding Algorithm (FISTA) algorithm into a deep network, named FISTA-Net to overcome the above shortcoming. FISTA-Net is formed from three modules, gradient descent module, proximal mapping module and accelerate module. Key parameters of FISTA-Net including the gradient step size, thresholding value are learned from training data. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can achieve a high-quality reconstruction result of BLT.
Fluorescence molecular tomography (FMT) is an important molecular imaging technique for tumor detection in early stage. In realistic research, due to the spread and metastasis of tumor cells, it is necessary to comprehensively analyze multiple tumor regions for cancer staging studies. Therefore, high-precision multi-light source reconstruction results are required for quantitative analysis in FMT research. However, the existing methods perform well in the reconstruction of single fluorescent source but may fail in reconstructing multiple targets, which is an obstacle for FMT practical application. In this paper, we proposed a multi-target reconstruction strategy for Fluorescence Molecular Tomography based on Blind Source Separation (BSS) by converting multi-target reconstructions into multiple single-target reconstructions. It is a breakthrough work in multi-target reconstruction for cancer staging using optical molecular technique. Numerical simulation experiments proved that it had the ability of multi-source resolution for FMT in accurate location and morphology recovery. The encouraging results demonstrate significant effectiveness and potential of our method for preclinical FMT applications.
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