Data-specific mask-guided image reconstruction for diffuse optical tomography

Combined multi-scale mesh and full-matrix inversion for enhancing time-domain breast diffuse optical tomography

Time-domain diffuse optical tomography can efficiently reconstruct both absorption and reduced scattering coefficients but is heavily limited by the ill-posedness in its inverse problem and low spatial resolution. To deal with these adversities, the truncated singular value decomposition (TSVD)-based whole-weighting-matrix inversion scheme can be a particularly suitable implementation. Unfortunately, TSVD is subject to a storage challenge for three-dimensional imaging of a bulk region, such as breast. In this paper, a multi-scale mesh strategy based on computed tomography (CT) anatomical geometry is adopted to solve the storage challenge, where a fine mesh is used in forward calculation to ensure accuracy, and a coarse mesh in the inversion process to enable TSVD-based inversion of the whole-weighting matrix. We validate the proposed strategy using simulated data for a single lesion model from clinical positron emission tomography images of a breast cancer patient, and further, for a complex model that is constructed by setting dual lesions at different separations in the CT breast geometry.

Interactive 3D reconstruction method of fuzzy static images in social media

Because the traditional social media fuzzy static image interactive three-dimensional (3D) reconstruction method has the problem of poor reconstruction completeness and long reconstruction time, the social media fuzzy static image interactive 3D reconstruction method is proposed. For preprocessing the fuzzy static image of social media, the Harris corner detection method is used to extract the feature points of the preprocessed fuzzy static image of social media. According to the extraction results, the parameter estimation algorithm of contrast divergence is used to learn the restricted Boltzmann machine (RBM) network model, and the RBM network model is divided into input, output, and hidden layers. By combining the RBM-based joint dictionary learning method and a sparse representation model, an interactive 3D reconstruction of fuzzy static images in social media is achieved. Experimental results based on the CAD software show that the proposed method has a reconstruction completeness of above 95% and the reconstruction time is less than 15 s, improving the completeness and efficiency of the reconstruction, effectively reconstructing the fuzzy static images in social media, and increasing the sense of reality of social media images.

One-dimensional convolutional neural network (1D-CNN) image reconstruction for electrical impedance tomography

In recent years, due to the strong autonomous learning ability of neural network algorithms, they have been applied for electrical impedance tomography (EIT). Although their imaging accuracy is greatly improved compared with traditional algorithms, generalization for both simulation and experimental data is required to be improved. According to the characteristics of voltage data collected in EIT, a one-dimensional convolutional neural network (1D-CNN) is proposed to solve the inverse problem of image reconstruction. Abundant samples are generated with numerical simulation to improve the edge-preservation of reconstructed images. The TensorFlow-graphics processing unit environment and Adam optimizer are used to train and optimize the network, respectively. The reconstruction results of the new network are compared with the Deep Neural Network (DNN) and 2D-CNN to prove the effectiveness and edge-preservation. The anti-noise and generalization capabilities of the new network are also validated. Furthermore, experiments with the EIT system are carried out to verify the practicability of the new network. The average image correlation coefficient of the new network increases 0.0320 and 0.0616 compared with the DNN and 2D-CNN, respectively, which demonstrates that the proposed method could give better reconstruction results, especially for the distribution of complex geometries.