Abstract
Low-dose digital radiography (DR) and computed tomography (CT) play a crucial role in minimizing health risks during clinical examinations and diagnoses. However, reducing the radiation dose often leads to lower signal-to-noise ratio measurements, resulting in degraded image quality. Existing supervised and self-supervised reconstruction techniques have been developed with noisy and clean image pairs or noisy and noisy image pairs, implying they cannot be adapted to single DR and CT image denoising. In this study, we introduce the Full Image-Index Remainder (FIRE) method. Our method begins by dividing the entire high-dimensional image space into multiple low-dimensional sub-image spaces using a full image-index remainder technique. By leveraging the data redundancy present within these sub-image spaces, we identify similar groups of noisy sub-images for training a self-supervised denoising network. Additionally, we establish a sub-space sampling theory specifically designed for self-supervised denoising networks. Finally, we propose a novel regularization optimization function that effectively reduces the disparity between self-supervised and supervised denoising networks, thereby enhancing denoising training. Through comprehensive quantitative and qualitative experiments conducted on both clinical low-dose CT and DR datasets, we demonstrate the remarkable effectiveness and advantages of our FIRE method compared to other state-of-the-art approaches.
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Acknowledgements
This work was supported in part by National Natural Science Foundation of China (grant numbers 62101611 and 62201628), National Key Research and Development Program of China (2022YFA1204200), Guangdong Basic and Applied Basic Research Foundation (grant number 2022A1515011375, 2023A1515012278, 2023A1515011780) and Shenzhen Science and Technology Program (grant number JCYJ20220530145411027, JCYJ20220818102414031).
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Long, Y., Pan, J., Xi, Y., Zhang, J., Wu, W. (2023). Full Image-Index Remainder Based Single Low-Dose DR/CT Self-supervised Denoising. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_44
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DOI: https://doi.org/10.1007/978-3-031-43990-2_44
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