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深度学习在医学成像中聚焦于改善计算机断层扫描图像质量的可能性。

Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality.

作者信息

Nakamura Yuko, Higaki Toru, Tatsugami Fuminari, Honda Yukiko, Narita Keigo, Akagi Motonori, Awai Kazuo

机构信息

From the Diagnostic Radiology, Hiroshima University, Hiroshima, Japan.

出版信息

J Comput Assist Tomogr. 2020 Mar/Apr;44(2):161-167. doi: 10.1097/RCT.0000000000000928.

Abstract

Deep learning (DL), part of a broader family of machine learning methods, is based on learning data representations rather than task-specific algorithms. Deep learning can be used to improve the image quality of clinical scans with image noise reduction. We review the ability of DL to reduce the image noise, present the advantages and disadvantages of computed tomography image reconstruction, and examine the potential value of new DL-based computed tomography image reconstruction.

摘要

深度学习(DL)是更广泛的机器学习方法家族的一部分,它基于学习数据表示而非特定任务的算法。深度学习可用于通过降低图像噪声来提高临床扫描的图像质量。我们回顾了深度学习降低图像噪声的能力,介绍了计算机断层扫描图像重建的优缺点,并探讨了基于深度学习的新型计算机断层扫描图像重建的潜在价值。

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