Sparse-View Spectral CT Reconstruction Using Deep Learning

Spectral CT is an emerging technology capable of providing high chemical specificity, which is crucial for many applications such as detecting threats in luggage. Such applications often require both fast and high-quality image reconstruction based on sparse-view (few) projections. The conventional FBP method is fast but it produces low-quality images dominated by noise and artifacts when few projections are available. Iterative methods with, e.g., TV regularizers can circumvent that but they are computationally expensive, with the computational load proportionally increasing with the number of spectral channels. Instead, we propose an approach for fast reconstruction of sparse-view spectral CT data using U-Net with multi-channel input and output. The network is trained to output high-quality images from input images reconstructed by FBP. The network is fast at run-time and because the internal convolutions are shared between the channels, the computation load increases only at the first and last layers, making it an efficient approach to process spectral data with a large number of channels. We validated our approach using real CT scans. The results show qualitatively and quantitatively that our approach is able to outperform the state-of-the-art iterative methods. Furthermore, the results indicate that the network is able to exploit the coupling of the channels to enhance the overall quality and robustness.