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用数据驱动方法重建的磁共振图像中的分辨率和噪声的评估
Jonas Kleineisel1, Katja Lauer2, Alfio Borzì3
1Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany; Institut Desbrest d'Épidémiologie et de Santé Publique, Univ Montpellier, INSERM, Montpellier, France.
Zeitschrift fur medizinische Physik
|September 8, 2023
概括
深度学习MRI重建方法比传统技术提供了更好的图像质量. 与压缩传感和GRAPPA相比,像VN和U-Net这样的数据驱动方法显示出较少的模糊和减少噪声增强.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 图像重建 图像的重建
背景情况:
- 加速MRI重建越来越多地使用深度学习.
- 对分辨率和信号噪声比的详细评估往往缺乏.
- 本研究通过评估常见的重建方法来解决这一差距.
研究的目的:
- 为了空间地映射图像分辨率和噪声增强 (g-因子).
- 评估典型的模型和数据驱动的MR重建方法.
- 为分析新的重建方法提供一个框架.
主要方法:
- 常规大脑MRI数据的回顾性下面样本 (R=4) .
- 使用变量网络 (VN),U-Net,GRAPPA和电视受约束的压缩传感重建.
- 通过点传播函数宽度进行本地分辨率估计;使用多个复制品进行g因子映射.
主要成果:
- 格拉帕的分辨率很好,但g因子 (1.43-1.84) 较高.
- 压缩传感导致局部分辨率较低,特别是在同质区域.
- VN和U-Net显示了可比的分辨率与中度模糊;U-Net略好一些.
- 分辨率和g因子根据CS,VN和U-Net的解剖学和低采样方向而有所不同.
结论:
- 确定了客观的质量参数 (分辨率,g因子).
- 数据驱动的方法 (VN,U-Net) 比压缩传感显示的模糊性要小.
- 噪声增强在CS,VN和U-Net的轮上升,但与GRAPPA相比降低.
- 该框架允许对先进的重建技术进行详细分析.
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