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Dual-Domain Self-Supervised Deep Learning with Graph Convolution for Low-Dose Computed Tomography Reconstruction.

Feng Yang^1,2, Feixiang Zhao¹, Yanhua Liu³

¹College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, No. 1 East 3rd Road, Erxianqiao, Chengdu, 610059, Sichuan, China.

Journal of Imaging Informatics in Medicine

|February 18, 2025

Summary

This summary is machine-generated.

This study introduces a novel dual-domain self-supervised framework (DDoS) for low-dose CT (LDCT) denoising and reconstruction. DDoS effectively enhances image quality by addressing noise in both sinogram and image domains, improving diagnostic accuracy.

Keywords:

Denoising Graph convolution Low-dose computed tomography Reconstruction Self-supervised deep learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Low-dose CT (LDCT) reduces radiation exposure but suffers from low signal-to-noise ratio (SNR), impacting diagnostic quality.
Existing deep learning denoising methods often require paired low-dose and normal-dose images, limiting clinical application.
Current self-supervised methods make simplistic noise assumptions and focus on single domains (sinogram or image), reducing effectiveness.

Purpose of the Study:

To develop an effective self-supervised deep learning framework for low-dose CT (LDCT) denoising and reconstruction.
To address the limitations of existing supervised and self-supervised denoising techniques in CT imaging.
To improve the diagnostic quality of LDCT images without requiring paired data.

Main Methods:

Introduced the Dual-Domain Self-supervised (DDoS) framework for LDCT denoising and reconstruction.
Developed sinogram-denoising and CT image-denoising networks tailored to specific noise characteristics.
Employed a unified hybrid architecture combining graph convolution and multi-channel attention for feature extraction in both domains.

Main Results:

The DDoS framework demonstrated superior performance in denoising and reconstruction compared to state-of-the-art methods.
Experiments on large-scale LDCT datasets validated the effectiveness of the dual-domain approach.
The method successfully enhanced SNR in LDCT images, meeting diagnostic quality standards.

Conclusions:

The DDoS framework offers a robust and effective self-supervised solution for LDCT image enhancement.
This approach overcomes the need for paired data, making it more clinically applicable.
DDoS significantly improves the diagnostic utility of low-dose CT scans.