摘 要
磁共振成像（MRI）技术作为现代医学影像领域的重要工具，在疾病诊断和临床研究中发挥着不可替代的作用，然而其图像质量常受到噪声、伪影及分辨率限制等问题的影响，从而制约了进一步的应用效果。为解决上述问题，本研究旨在提出一种基于深度学习的图像优化算法，以提升MRI图像的质量并增强其临床价值。具体而言，研究结合卷积神经网络（CNN）与生成对抗网络（GAN）的优势，设计了一种新型混合架构模型，该模型能够有效去除图像噪声、减少伪影干扰，并显著提高图像的空间分辨率。通过构建大规模高质量的MRI图像数据集进行训练与验证，实验结果表明，所提出的算法在信噪比、结构相似性指数等关键指标上均优于传统方法及现有主流算法。此外，该算法还具备良好的泛化能力，可适用于不同类型的MRI扫描设备和多种成像序列。
关键词：磁共振成像 深度学习 生成对抗网络
Abstract
Magnetic Resonance Imaging (MRI) technology, as a crucial tool in modern medical imaging, plays an irreplaceable role in disease diagnosis and clinical research. However, its image quality is often affected by issues such as noise, artifacts, and resolution limitations, which constrain further application effectiveness. To address these problems, this study aims to propose an image optimization algorithm based on deep learning to enhance the quality of MRI images and increase their clinical value. Specifically, the study combines the advantages of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to design a novel hybrid architecture model. This model can effectively remove image noise, reduce artifact interference, and significantly improve spatial resolution. By training and validating with large-scale high-quality MRI datasets, experimental results show that the proposed algorithm outperforms traditional methods and existing mainstream algorithms in key metrics such as signal-to-noise ratio and structural similarity index. Additionally, the algorithm demonstrates excellent generalization capabilities, making it suitable for various types of MRI scanners and multiple imaging sequences.
Keyword:Magnetic Resonance Imaging Deep Learning Generative Adversarial Network
目 录
1绪论 1
1.1磁共振成像技术的发展背景 1
1.2图像优化算法的研究意义 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2磁共振图像优化的基础理论 2
2.1磁共振成像的基本原理 2
2.2图像质量评估的关键指标 3
2.3常见图像优化算法分类 3
2.4数学模型在优化中的应用 4
2.5理论框架对研究的指导作用 4
3先进图像优化算法的设计与实现 4
3.1深度学习在磁共振图像中的应用 5
3.2基于卷积神经网络的优化方法 5
3.3自适应滤波算法的改进策略 6
3.4多尺度特征提取技术的应用 6
3.5算法性能的实验验证 7
4图像优化算法的实际应用与效果评估 7
4.1医学诊断中的图像优化需求 7
4.2临床数据集的处理与分析 8
4.3不同算法的对比实验研究 8
4.4实际应用场景的效果评估 8
4.5未来发展方向与潜在挑战 9
结论 9
参考文献 11
致谢 12
