Sparse-View Spectral CT Reconstruction Using Deep Learning

TL;DR

This paper introduces a deep learning-based method using a U-Net architecture for fast, high-quality spectral CT reconstruction from sparse-view data, outperforming traditional iterative techniques.

Contribution

The authors develop a multi-channel U-Net approach that efficiently reconstructs spectral CT images, leveraging channel coupling for improved quality and computational efficiency.

Findings

Outperforms state-of-the-art iterative methods in quality

Fast reconstruction suitable for real-time applications

Effectively exploits spectral channel coupling

Abstract

Spectral computed tomography (CT) is an emerging technology capable of providing high chemical specificity, which is crucial for many applications such as detecting threats in luggage. This type of application requires both fast and high-quality image reconstruction and is often based on sparse-view (few) projections. The conventional filtered back projection (FBP) method is fast but it produces low-quality images dominated by noise and artifacts in sparse-view CT. Iterative methods with, e.g., total variation regularizers can circumvent that but they are computationally expensive, as the computational load proportionally increases with the number of spectral channels. Instead, we propose an approach for fast reconstruction of sparse-view spectral CT data using a U-Net convolutional neural network architecture with multi-channel input and output. The network is trained to output high-quality CT images from FBP input image reconstructions. Our method is fast at run-time and because the internal convolutions are shared between the channels, the computational 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 have validated our approach using real CT scans. Our results show qualitatively and quantitatively that our approach outperforms the state-of-the-art iterative methods. Furthermore, the results indicate that the network can exploit the coupling of the channels to enhance the overall quality and robustness.