Enhancing low-dose CT images by 4x using CACTSR: a deep learning model

Yunhe Li; Mei Yang; Tao Bian; Haitao Wu

doi:10.1117/12.3050910

27 September 2024 Enhancing low-dose CT images by 4x using CACTSR: a deep learning model

Yunhe Li, Mei Yang, Tao Bian, Haitao Wu

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 132811A (2024) https://doi.org/10.1117/12.3050910
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

Computed Tomography is a crucial diagnostic tool in medicine but exposes patients to harmful radiation. Low-dose CT reduces radiation but leads to noisy images. Super-resolution technology can reconstruct high-resolution images from low-resolution scans, enhancing image quality while reducing radiation. This study proposes a novel deep learning model, CACTSR, which integrates VMamba and Transformer technologies with Mixed Attention Blocks and Cross Attention Blocks to enhance feature utilization and facilitate cross-window information interaction. Experimental results on the QING LUNG dataset demonstrate that CACTSR surpasses existing methods in terms of image quality metrics, generating images with crisp edges and abundant details. This innovative approach effectively mitigates block artifacts and enhances image quality, providing a powerful solution for reducing radiation doses in clinical CT imaging while maintaining diagnostic accuracy.

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

Citation Download Citation

Yunhe Li, Mei Yang, Tao Bian, and Haitao Wu "Enhancing low-dose CT images by 4x using CACTSR: a deep learning model", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 132811A (27 September 2024); https://doi.org/10.1117/12.3050910

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KEYWORDS

Computed tomography

Transformers

Image quality

Visualization

Deep learning

Super resolution

Feature extraction

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