摘要
X射线成像技术作为现代医学诊断与工业检测的核心工具，其技术原理的研究具有重要意义。本研究旨在深入探讨X射线成像设备的工作机制及其关键技术创新，以提升成像质量和效率。通过结合理论分析与实验验证，研究重点剖析了X射线的产生、传播特性以及探测器信号转换过程，并引入了一种新型的图像重建算法以优化图像分辨率。结果表明，该算法显著提高了低剂量条件下的图像清晰度，同时降低了噪声干扰。此外，本研究提出了一种基于深度学习的校正方法，有效解决了传统设备中常见的伪影问题。这一创新不仅为X射线成像设备的技术升级提供了新思路，还为低辐射剂量条件下的高质量成像奠定了理论基础。总体而言，本研究在提升成像精度和降低辐射风险方面做出了重要贡献，对推动X射线成像技术的实际应用具有深远意义。关键词：X射线成像；图像重建算法；深度学习校正；低剂量成像；伪影消除
Investigation into the Technical Principles of X-ray Imaging Devices
Abstract
X-ray imaging technology, as a core tool in modern medical diagnosis and industrial inspection, holds significant importance in the study of its technical principles. This research aims to explore the operational mechanisms of X-ray imaging devices and their key technological innovations to enhance imaging quality and efficiency. By integrating theoretical analysis with experimental validation, the study focuses on dissecting the generation and propagation characteristics of X-rays, as well as the signal conversion process in detectors, while introducing a novel image reconstruction algorithm to optimize image resolution. The results demonstrate that this algorithm substantially improves image clarity under low-dose conditions and effectively reduces noise interference. Additionally, a deep-learning-based correction method is proposed, successfully addressing common artifact issues in traditional equipment. This innovation not only provides new insights for the technological upgrading of X-ray imaging devices but also establishes a theoretical foundation for high-quality imaging under low-radiation-dose conditions. Overall, this research makes important contributions to improving imaging accuracy and reducing radiation risks, with profound implications for advancing the practical application of X-ray imaging technology.
Keywords: X-Ray Imaging; Image Reconstruction Algorithm; Deep Learning Correction; Low-Dose Imaging; Artifact Removal
目录
摘要 I
Abstract II
引言 1
1 X射线成像基础原理 1
1.1 成像基本原理探讨 1
1.2 关键技术参数研究 2
2 X射线探测器技术原理 2
2.1 探测器材料与性能 2
2.2 信号转换机制分析 3
2.3 探测效率优化策略 3
3 图像重建算法原理 4
3.1 常用算法分类与特点 4
3.2 数学模型构建方法 4
3.3 算法精度提升途径 5
4 设备系统集成原理 5
4.1 系统架构设计原则 5
4.2 核心组件协同机制 6
4.3 整体性能评估方法 6
结论 6
参考文献 8
致谢 9
