Projected algebraic reconstruction technique-network for high-fidelity diffuse fluorescence tomography reconstruction.

We propose a model-driven projected algebraic reconstruction technique (PART)-network (PART-Net) that leverages the advantages of the traditional model-based method and the neural network to improve the imaging quality of diffuse fluorescence tomography. In this algorithm, nonnegative prior information is incorporated into the ART iteration process to better guide the optimization process, and thereby improve imaging quality. On this basis, PART in conjunction with a residual convolutional neural network is further proposed to obtain high-fidelity image reconstruction. The numerical simulation results demonstrate that the PART-Net algorithm effectively improves noise robustness and reconstruction accuracy by at least 1-2 times and exhibits superiority in spatial resolution and quantification, especially for a small-sized target (r=2m m), compared with the traditional ART algorithm. Furthermore, the phantom and in vivo experiments verify the effectiveness of the PART-Net, suggesting strong generalization capability and a great potential for practical applications.

Performance enhancement of diffuse fluorescence tomography based on an extended Kalman filtering-long short term memory neural network correction model.

To alleviate the ill-posedness of diffuse fluorescence tomography (DFT) reconstruction and improve imaging quality and speed, a model-derived deep-learning method is proposed by combining extended Kalman filtering (EKF) with a long short term memory (LSTM) neural network, where the iterative process parameters acquired by implementing semi-iteration EKF (SEKF) served as inputs to the LSTM neural network correction model for predicting the optimal fluorescence distributions. To verify the effectiveness of the SEKF-LSTM algorithm, a series of numerical simulations, phantom and in vivo experiments are conducted, and the experimental results are quantitatively evaluated and compared with the traditional EKF algorithm. The simulation experimental results show that the proposed new algorithm can effectively improve the reconstructed image quality and reconstruction speed. Importantly, the LSTM correction model trained by the simulation data also obtains satisfactory results in the experimental data, suggesting that the SEKF-LSTM algorithm possesses strong generalization ability and great potential for practical applications.

IR780-based diffuse fluorescence tomography for cancer detection.

IR780 iodide is a commercially available targeted near-infrared contrast agent for in vivo imaging and cancer photodynamic or photothermal therapy, whereas the accumulation, dynamics, and retention of IR780 in biological tissue, especially in tumor is still under-explored. Diffuse fluorescence tomography (DFT) can be used for localization and quantification of the three-dimensional distribution of NIR fluorophores. Herein, a homemade DFT imaging system combined with tumor-targeted IR780 was utilized for cancer imaging and pharmacokinetic evaluation. The aim of this study is to comprehensively assess the biochemical and pharmacokinetic characteristics of IR780 with the aid of DFT imaging. The optimal IR780 concentration (20 µg/mL) was achieved first. Subsequently, the good biocompatibility and cellar uptake of IR780 was demonstrated through the mouse acute toxic test and cell assay. In vivo, DFT imaging effectively identified various subcutaneous tumors and revealed the long-term retention of IR780 in tumors and rapid metabolism in the liver. Ex vivo imaging indicated IR780 was mainly concentrated in tumor and lung with significantly different from the distribution in other organs. DFT imaging allowed sensitive tumor detection and pharmacokinetic rates analysis. Simultaneously, the kinetics of IR780 in tumors and liver provided more valuable information for application and development of IR780.