Sinogram denoising via attention residual dense convolutional neural network for low-dose computed tomography

The widespread use of computed tomography (CT) in clinical practice has made the public focus on the cumulative radiation dose delivered to patients. Low-dose CT (LDCT) reduces the X-ray radiation dose, yet compromises quality and decreases diagnostic performance. Researchers have made great efforts to develop various algorithms for LDCT and introduced deep-learning techniques, which have achieved impressive results. However, most of these methods are directly performed on reconstructed LDCT images, in which some subtle structures and details are readily lost during the reconstruction procedure, and convolutional neural network (CNN)-based methods for raw LDCT projection data are rarely reported. To address this problem, we adopted an attention residual dense CNN, referred to as AttRDN, for LDCT sinogram denoising. First, it was aided by the attention mechanism, in which the advantages of both feature fusion and global residual learning were used to extract noise from the contaminated LDCT sinograms. Then, the denoised sinogram was restored by subtracting the noise obtained from the input noisy sinogram. Finally, the CT image was reconstructed using filtered back-projection. The experimental results qualitatively and quantitatively demonstrate that the proposed AttRDN can achieve a better performance than state-of-the-art methods. Importantly, it can prevent the loss of detailed information and has the potential for clinical application.

Automated summary

The use of deep learning techniques in low-dose CT imaging shows potential for clinical application by reducing radiation dose while maintaining image quality.