Low-dose CT denoising network combined with attention mechanism

Xinru Zhan

doi:10.1117/12.3035371

19 July 2024 Low-dose CT denoising network combined with attention mechanism

Xinru Zhan

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132133Q (2024) https://doi.org/10.1117/12.3035371
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Computed tomography (CT) technology is widely used, but the X-ray radiation it emits is a concern. As a result, more and more research is focusing on how to maintain the quality of CT images while reducing the X-ray dose. With the advancement of deep learning technology, convolutional neural networks (CNNs) have been extensively applied in the field of CT reconstruction. However, CNNs focus only on local information and do not adequately consider the overall structure of the image. To address this issue, we have introduced an attention mechanism into the convolutional neural network to denoise low-dose CT images. We tested and evaluated the proposed denoising method using the AAPM-Mayo Clinic low-dose CT dataset. The experimental results show that our method can effectively remove stripe artifacts in LDCT images, preserve image details to a certain extent, and improve several metrics such as PSNR, SSIM, and RMSE compared to LDCT images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinru Zhan "Low-dose CT denoising network combined with attention mechanism", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132133Q (19 July 2024); https://doi.org/10.1117/12.3035371

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