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In practical scenarios, sparse view computed tomography (CT) is a successful method for reducing the X-ray radiation dose and imaging time. Generating high-quality images with undersampled projection data using conventional techniques is difficult. To reconstruction high quality CT image from the situation of undersampling, we proposed a weighted sparse constraint reconstruction (WSCR) framework in this work. Motivated by the concept of deep convolutional neural network, we expand the iterative reconstruction scheme for constructing reweighted sparse representation statistically, with a specific number of iterations for training based on data-driven approach, resulting in the development of a reconstruction network based on WSCR. The WSCR network is composed of several iteration blocks, with each block containing two modules: image reconstruction and sparse representation network modules. The CT image is updated using a deep learning-based prior constraint in the image reconstruction module. To further restrict the process of reconstructing the image, the reweighted feature map and filter updating are implemented using a sparse representation network. The results of the experiment showed that our suggested WCSR network was able to achieve superior performance in removing artifacts and preserving edges.
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