Department of Computer Science & Engineering, Punjabi University, Chandigarh Road, Patiala 147002, Punjab, India.
Phys Med Biol. 2024 Nov 28;69(23). doi: 10.1088/1361-6560/ad94c7.
Magnetic resonance imaging (MRI) provides detailed structural information of the internal body organs and soft tissue regions of a patient in clinical diagnosis for disease detection, localization, and progress monitoring. MRI scanner hardware manufacturers incorporate various post-acquisition image-processing techniques into the scanner's computer software tools for different post-processing tasks. These tools provide a final image of adequate quality and essential features for accurate clinical reporting and predictive interpretation for better treatment planning. Different post-acquisition image-processing tasks for MRI quality enhancement include noise removal, motion artifact reduction, magnetic bias field correction, and eddy electric current effect removal. Recently, deep learning (DL) methods have shown great success in many research fields, including image and video applications. DL-based data-driven feature-learning approaches have great potential for MR image denoising and image-quality-degrading artifact correction. Recent studies have demonstrated significant improvements in image-analysis tasks using DL-based convolutional neural network techniques. The promising capabilities and performance of DL techniques in various problem-solving domains have motivated researchers to adapt DL methods to medical image analysis and quality enhancement tasks. This paper presents a comprehensive review of DL-based state-of-the-art MRI quality enhancement and artifact removal methods for regenerating high-quality images while preserving essential anatomical and physiological feature maps without destroying important image information. Existing research gaps and future directions have also been provided by highlighting potential research areas for future developments, along with their importance and advantages in medical imaging.
磁共振成像（MRI）为临床诊断中疾病的检测、定位和进展监测提供了患者内部器官和软组织区域的详细结构信息。MRI 扫描仪硬件制造商将各种后获取图像处理技术纳入扫描仪的计算机软件工具中，用于不同的后处理任务。这些工具提供了具有足够质量和必要特征的最终图像，可用于准确的临床报告和预测解释，以制定更好的治疗计划。用于 MRI 质量增强的不同后获取图像处理任务包括噪声去除、运动伪影减少、磁场偏置场校正和涡流电流效应去除。最近，深度学习（DL）方法在许多研究领域取得了巨大成功，包括图像和视频应用。基于 DL 的数据驱动特征学习方法在磁共振图像去噪和图像质量降低伪影校正方面具有很大的潜力。最近的研究表明，使用基于 DL 的卷积神经网络技术在图像分析任务中取得了显著的改进。DL 技术在解决各种问题方面的有前途的能力和性能促使研究人员将 DL 方法应用于医学图像分析和质量增强任务。本文全面回顾了基于 DL 的最先进的 MRI 质量增强和伪影去除方法，这些方法可在不破坏重要图像信息的情况下，生成高质量的图像，同时保留重要的解剖学和生理学特征图。通过突出未来发展的潜在研究领域，以及它们在医学成像中的重要性和优势，本文还提供了现有的研究差距和未来方向。
