利用深度学习对 CT 图像进行去噪和去模糊：现状与展望

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2023-12-12 DOI:10.1109/TRPMS.2023.3341903

Yiming Lei;Chuang Niu;Junping Zhang;Ge Wang;Hongming Shan

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引用次数: 0

摘要

本文回顾了分别和同时用于计算机断层扫描图像去噪和去模糊的深度学习方法。然后，我们讨论了该领域的发展方向，如与大规模预训练模型和大型语言模型相结合。目前，深度学习正在以数据驱动的方式彻底改变医学成像。随着学习范式的快速发展，相关算法和模型在临床应用方面也取得了突飞猛进的进展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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CT Image Denoising and Deblurring With Deep Learning: Current Status and Perspectives

This article reviews the deep learning methods for computed tomography image denoising and deblurring separately and simultaneously. Then, we discuss promising directions in this field, such as a combination with large-scale pretrained models and large language models. Currently, deep learning is revolutionizing medical imaging in a data-driven manner. With rapidly evolving learning paradigms, related algorithms and models are making rapid progress toward clinical applications.

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来源期刊

IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

8.00

自引率

18.20%

发文量

109