Low-dose CT reconstruction using cross-domain deep learning with domain transfer module

Abstract Objective: X-ray computed tomography employing low-dose X-ray source is actively researched to reduce radiation exposure. However, the reduced photon count in low-dose X-ray sources leads to severe noise artifacts in analytic reconstruction methods like filtered backprojection. Recently, deep learning-based approaches employing uni-domain networks, either in the image-domain or projection-domain, have demonstrated remarkable effectiveness in reducing image noise and Poisson noise caused by low-dose X-ray source. Furthermore, dual-domain networks that integrate image-domain and projection-domain networks are being developed to surpass the performance of uni-domain networks. Despite this advancement, dual-domain networks require twice the computational resources of uni-domain networks, even though their underlying network architectures are not substantially different.

Approach: The U-Net architecture, a type of Hourglass network, comprises encoder and decoder modules. The encoder extracts meaningful representations from the input data, while the decoder uses these representations to reconstruct the target data. In dual-domain networks, however, encoders and decoders are redundantly utilized due to the sequential use of two networks, leading to increased computational demands. To address this issue, this study proposes a cross-domain deep learning approach that leverages analytical domain transfer functions. These functions enable the transfer of features extracted by an encoder trained in input domain to target domain, thereby reducing redundant computations. The target data is then reconstructed using a decoder trained in the corresponding domain, optimizing resource efficiency without compromising performance.

Main Results: The proposed cross-domain network, comprising a projection-domain encoder and an image-domain decoder, demonstrated effective performance by leveraging the domain transfer function, achieving comparable results with only half the trainable parameters of dual-domain networks. Moreover, the proposed method outperformed conventional iterative reconstruction techniques and existing deep learning approaches in reconstruction quality.

Significance: The proposed network leverages the transfer function to bypass redundant encoder and decoder modules, enabling direct connections between different domains. This approach not only surpasses the performance of dual-domain networks but also significantly reduces the number of required parameters. By facilitating the transfer of primal representations across domains, the method achieves synergistic effects, delivering high quality reconstruction images with reduced radiation doses.