Self-supervised dual-domain balanced dropblock-network for low-dose CT denoising

An, Ran and Chen, Ke and Li, Hongwei (2024) Self-supervised dual-domain balanced dropblock-network for low-dose CT denoising. Physics in Medicine and Biology, 69 (7). 075026. ISSN 0031-9155 (https://doi.org/10.1088/1361-6560/ad29ba)

[thumbnail of An-etal-PMB-2024-Self-supervised-dual-domain-balanced-dropblock-network-for-low-dose-CT-denoising] Text. Filename: An-etal-PMB-2024-Self-supervised-dual-domain-balanced-dropblock-network-for-low-dose-CT-denoising.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 26 March 2025.
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (2MB) | Request a copy

Abstract

Objective. Self-supervised learning methods have been successfully applied for low-dose computed tomography (LDCT) denoising, with the advantage of not requiring labeled data. Conventional self-supervised methods operate only in the image domain, ignoring valuable priors in the sinogram domain. Recently proposed dual-domain methods address this limitation but encounter issues with blurring artifacts in the reconstructed image due to the inhomogeneous distribution of noise levels in low-dose sinograms. Approach. To tackle this challenge, this paper proposes SDBDNet, an end-to-end dual-domain self-supervised method for LDCT denoising. With the network designed based on the properties of inhomogeneous noise in low-dose sinograms and the principle of moderate sinogram-domain denoising, SDBDNet achieves effective denoising in dual domains without introducing blurring artifacts. Specifically, we split the sinogram into two subsets based on the positions of detector cells to generate paired training data with high similarity and independent noise. These sub-sinograms are then restored to their original size using 1D interpolation and learning-based correction. To achieve adaptive and moderate smoothing in the sinogram domain, we integrate Dropblock, a type of convolution layer with regularization, into SDBDNet, and set a weighted average between the denoised sinograms and their noisy counterparts, leading to a well-balanced dual-domain approach. Main results. Numerical experiments show that our method outperforms popular non-learning and self-supervised learning methods, demonstrating its effectiveness and superior performance. Significance. While introducing a novel high-performance dual-domain self-supervised LDCT denoising method, this paper also emphasizes and verifies the importance of appropriate sinogram-domain denoising in dual-domain methods, which might inspire future work.

  • Item type:Article
    ID code:88706
    Dates:
    Date
    Event
    7 April 2024
    Published
    26 March 2024
    Published Online
    15 February 2024
    Accepted
    6 November 2023
    Submitted
    Subjects:Science > Mathematics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:15 Apr 2024 11:38
    Last modified:17 Apr 2024 14:52
    URI:https://strathprints.strath.ac.uk/id/eprint/88706
CORE (COnnecting REpositories)