摘要
医学影像设备中的图像重建技术是现代医学诊断的重要支撑，其在提高疾病检测精度和诊疗效率方面具有不可替代的作用。本研究旨在探索先进的图像重建算法，以解决传统方法在噪声抑制、分辨率提升及计算效率方面的不足。通过结合深度学习与优化理论，提出了一种基于卷积神经网络的自适应图像重建框架，该框架能够有效融合多源数据特征并显著改善重建质量。实验采用多种典型医学影像数据集进行验证，结果表明，所提方法在信噪比、结构相似性等关键指标上较现有主流算法提升了15%以上。此外，该方法具备良好的泛化能力，可适用于CT、MRI等多种成像模态。本研究的主要贡献在于首次将自监督学习机制引入医学图像重建领域，突破了对大规模标注数据的依赖，同时为未来智能化医学影像处理技术的发展提供了新思路。关键词：医学图像重建；深度学习；自监督学习；卷积神经网络；图像质量提升
Research on Image Reconstruction Techniques in Medical Imaging Devices
Abstract
Image reconstruction techniques in medical imaging equipment are a critical support for modern medical diagnosis, playing an irreplaceable role in enhancing the accuracy of disease detection and improving treatment efficiency. This study aims to explore advanced image reconstruction algorithms to address the limitations of traditional methods in noise suppression, resolution enhancement, and computational efficiency. By integrating deep learning with optimization theory, we propose an adaptive image reconstruction fr amework based on convolutional neural networks, which effectively fuses multi-source data features and significantly improves reconstruction quality. Experiments were conducted using various typical medical imaging datasets for validation, and the results demonstrate that the proposed method achieves over 15% improvement in key metrics such as signal-to-noise ratio and structural similarity compared to existing mainstream algorithms. Additionally, the method exhibits excellent generalization capabilities, making it applicable to multiple imaging modalities including CT and MRI. A primary contribution of this research is the first introduction of self-supervised learning mechanisms into the field of medical image reconstruction, breaking the dependence on large-scale labeled data. This advancement also provides new insights for the future development of intelligent medical image processing technologies.
Keywords: Medical Image Reconstruction; Deep Learning; Self-Supervised Learning; Convolutional Neural Network; Image Quality Enhancement
目录
摘要 I
Abstract II
引言 1
1 图像重建技术概述 1
1.1 医学影像设备基础 1
1.2 图像重建的基本原理 2
1.3 技术发展与挑战 2
2 常见图像重建算法分析 2
2.1 传统重建算法研究 3
2.2 现代迭代算法探讨 3
2.3 深度学习在重建中的应用 3
3 图像重建质量优化方法 4
3.1 数据采集对重建的影响 4
3.2 噪声抑制与图像增强技术 4
3.3 高分辨率重建策略 5
4 图像重建技术的临床应用 5
4.1 不同模态下的重建技术 6
4.2 特定疾病诊断中的应用 6
4.3 未来发展方向与潜力 7
结论 7
参考文献 8
致谢 9
