Medical imaging plays a critical role in diagnosing and treating various medical conditions. However, interpreting medical images can be challenging even for expert clinicians, as they are often degraded by noise and artifacts that can hinder the accurate identification and analysis of diseases, leading to severe consequences such as patient misdiagnosis or mortality. Various types of noise, including Gaussian, Rician, and Salt-pepper noise, can corrupt the area of interest, limiting the precision and accuracy of algorithms. Denoising algorithms have shown the potential in improving the quality of medical images by removing noise and other artifacts that obscure essential information. Deep learning has emerged as a powerful tool for image analysis and has demonstrated promising results in denoising different medical images such as MRIs, CT scans, PET scans, etc. This review paper provides a comprehensive overview of state-of-the-art deep learning algorithms used for denoising medical images. A total of 120 relevant papers were reviewed, and after screening with specific inclusion and exclusion criteria, 104 papers were selected for analysis. This study aims to provide a thorough understanding for researchers in the field of intelligent denoising by presenting an extensive survey of current techniques and highlighting significant challenges that remain to be addressed. The findings of this review are expected to contribute to the development of intelligent models that enable timely and accurate diagnoses of medical disorders. It was found that 40% of the researchers used models based on Deep convolutional neural networks to denoise the images, followed by encoder-decoder (18%) and other artificial intelligence-based techniques (15%) (Like DIP, etc.). Generative adversarial network was used by 12%, transformer-based approaches (13%) and multilayer perceptron was used by 2% of the researchers. Moreover, Gaussian noise was present in 35% of the images, followed by speckle noise (16%), poisson noise (14%), artifacts (10%), rician noise (7%), Salt-pepper noise (6%), Impulse noise (3%) and other types of noise (9%). While the progress in developing novel models for the denoising of medical images is evident, significant work remains to be done in creating standardized denoising models that perform well across a wide spectrum of medical images. Overall, this review highlights the importance of denoising medical images and provides a comprehensive understanding of the current state-of-the-art deep learning algorithms in this field.

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使用深度学习对医学图像去噪的最新进展：模型、技术和挑战概述

医学成像在诊断和治疗各种医疗状况中发挥着至关重要的作用。然而，即使对于专业临床医生来说，解读医学图像也具有挑战性，因为它们经常会受到噪声和伪影的影响，从而阻碍疾病的准确识别和分析，从而导致患者误诊或死亡等严重后果。各种类型的噪声，包括高斯噪声、莱斯噪声和椒盐噪声，可能会破坏感兴趣的区域，从而限制算法的精度和准确度。去噪算法已显示出通过消除噪声和其他掩盖基本信息的伪影来提高医学图像质量的潜力。深度学习已成为图像分析的强大工具，并在不同医学图像（如 MRI、CT 扫描、PET 扫描等）去噪方面展示了良好的结果。这篇综述论文全面概述了最先进的深度学习用于医学图像去噪的算法。共审阅相关论文120篇，经特定纳入和排除标准筛选后，选取104篇论文进行分析。本研究旨在通过对当前技术进行广泛的调查并强调仍有待解决的重大挑战，为智能去噪领域的研究人员提供全面的了解。这篇综述的结果预计将有助于智能模型的开发，从而能够及时、准确地诊断医疗疾病。研究发现，40％的研究人员使用基于深度卷积神经网络的模型对图像进行去噪，其次是编码器-解码器（18％）和其他基于人工智能的技术（15％）（如DIP等）。 12% 的研究人员使用生成对抗网络，2% 的研究人员使用基于变压器的方法 (13%) 和多层感知器。此外，35% 的图像中存在高斯噪声，其次是散斑噪声 (16%)、泊松噪声 (14%)、伪影 (10%)、莱斯噪声 (7%)、椒盐噪声 (6%) 、脉冲噪声（3%）和其他类型的噪声（9%）。虽然开发用于医学图像去噪的新颖模型的进展是显而易见的，但在创建在广泛的医学图像中表现良好的标准化去噪模型方面仍有大量工作要做。总的来说，这篇综述强调了医学图像去噪的重要性，并提供了对该领域当前最先进的深度学习算法的全面理解。