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Self-supervised denoising of projection data for low-dose cone-beam CT.基于投影数据的自我监督去噪在低剂量锥形束 CT 中的应用。
Med Phys. 2023 Oct;50(10):6319-6333. doi: 10.1002/mp.16421. Epub 2023 Apr 20.
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Noise Suppression With Similarity-Based Self-Supervised Deep Learning.基于相似性的自监督深度学习的噪声抑制。
IEEE Trans Med Imaging. 2023 Jun;42(6):1590-1602. doi: 10.1109/TMI.2022.3231428. Epub 2023 Jun 1.
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MDST: multi-domain sparse-view CT reconstruction based on convolution and swin transformer.MDST:基于卷积和 Swin Transformer 的多域稀疏视图 CT 重建。
Phys Med Biol. 2023 Apr 26;68(9). doi: 10.1088/1361-6560/acc2ab.
5
CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising.CTformer:用于低剂量 CT 去噪的无卷积 Token2Token 扩张视觉转换器。
Phys Med Biol. 2023 Mar 15;68(6). doi: 10.1088/1361-6560/acc000.
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STEDNet: Swin transformer-based encoder-decoder network for noise reduction in low-dose CT.STEDNet:基于 Swin Transformer 的编解码网络,用于降低低剂量 CT 中的噪声。
Med Phys. 2023 Jul;50(7):4443-4458. doi: 10.1002/mp.16249. Epub 2023 Feb 9.
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Low-Dose CT Denoising via Sinogram Inner-Structure Transformer.基于正弦图内部结构变换器的低剂量CT去噪
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8
Transformer With Double Enhancement for Low-Dose CT Denoising.用于低剂量CT去噪的双增强Transformer
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9
A Comparative Study between Image- and Projection-Domain Self-Supervised Learning for Ultra Low-Dose CBCT.基于图像域和投影域的自我监督学习在超低剂量锥形束 CT 中的对比研究。
Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:2076-2079. doi: 10.1109/EMBC48229.2022.9871947.
10
Self-supervised Projection Denoising for Low-Dose Cone-Beam CT.基于自监督投影去噪的低剂量锥形束 CT 重建方法。
Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3459-3462. doi: 10.1109/EMBC46164.2021.9629859.

用于CT图像去噪和重建的自监督学习:综述

Self-supervised learning for CT image denoising and reconstruction: a review.

作者信息

Choi Kihwan

机构信息

Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811 Republic of Korea.

出版信息

Biomed Eng Lett. 2024 Sep 12;14(6):1207-1220. doi: 10.1007/s13534-024-00424-w. eCollection 2024 Nov.

DOI:10.1007/s13534-024-00424-w
PMID:39465103
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11502646/
Abstract

This article reviews the self-supervised learning methods for CT image denoising and reconstruction. Currently, deep learning has become a dominant tool in medical imaging as well as computer vision. In particular, self-supervised learning approaches have attracted great attention as a technique for learning CT images without clean/noisy references. After briefly reviewing the fundamentals of CT image denoising and reconstruction, we examine the progress of deep learning in CT image denoising and reconstruction. Finally, we focus on the theoretical and methodological evolution of self-supervised learning for image denoising and reconstruction.

摘要

本文综述了用于CT图像去噪和重建的自监督学习方法。目前,深度学习已成为医学成像以及计算机视觉中的主导工具。特别是,自监督学习方法作为一种无需干净/有噪声参考即可学习CT图像的技术,已引起了广泛关注。在简要回顾CT图像去噪和重建的基本原理之后,我们研究了深度学习在CT图像去噪和重建方面的进展。最后,我们重点关注用于图像去噪和重建的自监督学习的理论和方法演变。