Tensor convolutional neural network architecture for spectral CT reconstruction

Photon-counting spectral computed tomography (PCCT) reconstructs multiple energy-channel images to describe the same object, where there exists a strong correlation among all channels. In addition, reconstruction of each energy-channel image suffers photon count starving problem. To make full use of the correlation among different channels to suppress the data noise and enhance the tissue texture in reconstructing each energy-channel image, this paper proposed a tensor convolutional neural network (TCNN) architecture to learn a tissue-specific texture prior for PCCT reconstruction. Specifically, we first model the spatial texture prior information in each individual channel using a convolution neural network, and then extract the correlation information among different energy channels by merging the multi-channel networks. Finally, we integrate the TCNN as a prior into Bayesian reconstruction framework. To evaluate the tissue texture preserving performance of the proposed method for each channel, a vivid clinical phantom which can simulate the real tissue textures was employed. The improvement associated with TCNN is remarkable relative to simultaneous algebraic reconstruction technique (SART) and tensor dictionary learning (TDL) based reconstruction. The proposed method produced promising results in terms of not only preserving texture feature but also suppressing image noise in each channel. The proposed method outperforms the competing methods in both visual inspection and quantitative indexes of root mean square error (RMSE), peak signal to noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM).