Low-dose CT denoising based on multihead self-attention and multiscale feature fusion

Yi Deng; Xingcheng Pu

doi:10.1117/12.3031313

19 July 2024 Low-dose CT denoising based on multihead self-attention and multiscale feature fusion

Yi Deng, Xingcheng Pu

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131812E (2024) https://doi.org/10.1117/12.3031313
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Computed tomography (CT) is a commonly used medical imaging modality for diagnostic purposes. However, the high radiation dose associated with CT scans can pose risks to the human body. Furthermore, reducing the radiation dose can significantly impact the quality of CT images, leading to a decline in image quality. To enhance the denoising performance of Low-Dose CT (LDCT) and improve the quality of CT images, a novel multi-head self-attention mechanism and multiscale feature fusion network (MAMSNet) is proposed on basis of encoder-decoder network architecture. In new MAMSNet, there are threefold modifications. Firstly, a multi-head self-attention(MHSA) block is introduced to improve the global extraction feature and achieve better denoising effect. Secondly, to enhance the receptive field and obtain information at multiple scales, a block called multi-scale feature fusion (MSFF) is utilized. Moreover, a Structure-aware loss function is added to maintain the texture structure information and elevate the image fidelity of CT scans. The improved method can be effectively used to remove the noise and artifacts of LDCT images. Compared with other methods, more texture features, structural details and higher objective metrics (such as PSNR and SSIM) can be preserved.

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

Citation Download Citation

Yi Deng and Xingcheng Pu "Low-dose CT denoising based on multihead self-attention and multiscale feature fusion", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131812E (19 July 2024); https://doi.org/10.1117/12.3031313

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KEYWORDS

Denoising

Computed tomography

Feature extraction

Feature fusion

Convolution

Reconstruction algorithms

Education and training

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