利用神经网络方法对低剂量发射断层扫描重建后去噪的综述

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Alexandre Bousse;Venkata Sai Sundar Kandarpa;Kuangyu Shi;Kuang Gong;Jae Sung Lee;Chi Liu;Dimitris Visvikis
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引用次数: 0

摘要

低剂量发射断层扫描(ET)在医学成像中起着至关重要的作用,它能获取各种生物过程的功能信息,同时最大限度地减少病人的剂量。然而,光子计数过程中固有的随机性是噪声的来源之一,而低剂量 ET 会放大这种噪声。这篇综述文章概述了现有的后处理技术,重点介绍了深度神经网络 (NN) 方法。此外,我们还探讨了基于 NN 的低剂量 ET 领域的未来发展方向。这一全面研究揭示了深度学习在提高低剂量 ET 图像质量和分辨率方面的潜力,最终推动医学成像领域的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches
Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
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
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