深度学习图像重建算法的影响通过相位解析功能性肺 (PREFUL) 磁共振成像 (MRI) 在潜在健康队列中获得的通风和 perfusion 参数
J Kroschke1, Y Huber1, B Kerber1
1Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Clinical radiology
|February 5, 2026
概括
深度学习 (DL) 重建改善了肺部MRI图像质量,而不影响阶段解析功能肺部 (PREFUL) MRI 的通风或 perfusion 测量. 这种先进的技术可以安全地集成到功能性肺部MRI工作流程中,以加强诊断.
科学领域:
- 医疗成像医学成像
- 放射学 放射学是一门学科.
- 深度学习应用程序
背景情况:
- 基于深度学习 (DL) 的重建算法在临床实践中越来越多地用于MRI.
- 这些算法有可能减少成像时间和提高图像质量.
- DL重建对功能性肺MRI参数的影响尚未完全理解.
研究的目的:
- 评估DL重建的磁共振成像 (MRI) 图像是否会影响从相位解析功能肺 (PREFUL) 导出的通风和 perfusion 参数.
- 为了比较传统和基于DL的重建方法之间的图像质量指标.
- 评估将DL重建整合到功能性肺MRI中的安全性和有效性.
主要方法:
- 包括24名正常肺功能健康志愿者的前性研究.
- 在1.5T时进行的自由呼吸肺部MRI.
- 使用传统非DL和DL方法重建的原始数据.
- 由SNR和CNR评估的图像质量.
- PREFUL MRI用于获得Rvent,FVL-CM,Q,VDPs,QDPs和V / Q匹配.
- 使用配对测试和布兰德-阿尔特曼分析进行统计比较.
主要成果:
- 基于DL的重建显著改善了图像质量 (SNR和CNR,P<.001).
- 在DL和非DL重建之间的任何PREFUL衍生参数中没有观察到任何显著差异.
- 布兰德-阿尔特曼分析表明,在所有功能指标上,一致性很好,偏见最小.
- 在重建方法之间发现了强到近乎完美的相关性 (r=0.8998到0.9998,P<.001).
结论:
- 基于DL的重建可以提高肺部MRI图像质量,而不会改变通风或 perfusion 量化.
- 这种方法可以安全地集成到功能性肺部MRI工作流程中,用于正常肺功能的人.
- 结果证实了DL重建的图像特征的保存以及PREFUL方法的稳定性.
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