SureUnet: sparse autorepresentation encoder U-Net for noise artifact suppression in low-dose CT

Low-dose computed tomography (LDCT) is desirable due to ionizing radiation, but the resulting images suffer from serious streak artifacts and spot noise. Recently, deep learning (DL)-based methods have emerged as promising alternatives for medical image processing. However, most DL-based methods are built intuitively and lack interpretability, and it is difficult to effectively separate the artifacts and noise in LDCT images. Obtaining diagnostically useful images, especially when using a low-dose scanner protocol, remains an open challenge. To improve the quality of LDCT images, we developed a novel processing network called the sparse autorepresentation U-Net (SureUnet). First, inspired by multilayer convolutional sparse coding (CSC), we constructed a sparse autorepresentation encoder to sufficiently capture and represent hierarchical image features. Then, we chose the widely used U-Net model for sparse autorepresentation block applications and designed SureUnet by adding a feature decoding block. Therefore, every module has well-defined interpretability in our network. Additionally, hybrid loss functions were specifically designed, including the mean absolute error, edge loss and perceptual loss. Through the cooperation of multiple loss functions, the noise artifact suppression effect of the network was improved. The visual results obtained on the MAYO and UIH datasets show that the proposed method’s noise artifact suppression effect was more significant. The quantitative results showed promising improvement levels compared to those of the other state-of-the-art methods. The SureUnet model significantly outperformed the compared methods on two datasets, with margins of 0.4 dB for the PSNR, 0.007 for the SSIM, and 1.6 for the FID on the MAYO dataset and margins of 0.5 dB for the PSNR, 0.004 for the SSIM and 2.9 for the FID on the UIH dataset. This work paves the way for sparse autorepresentation in DL for processing LDCT images. Experimental results have demonstrated the competitive performance of SureUnet in terms of noise suppression, structural fidelity and visual impression improvement.