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加速非对比增强三维心血管磁共振深度学习重建

Sukran Erdem¹, Orhan Erdem², M Tarique Hussain^1,3,4

¹Division of Pediatric Cardiology, Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, TX 75235, USA.

Reviews in cardiovascular medicine

|August 8, 2025

概括

适应性CS-Net显著提高了心血管磁共振成像质量,用于使用REACT技术进行全心成像. 与标准方法相比,这种深度学习算法可以提高肺静脉和胸腔血管的图像质量.

科学领域:

医疗成像医学成像
心血管成像 - 心血管成像
人工智能在医学中的应用

背景情况:

心血管磁共振 (CMR) 是非常重要的,但耗时.
快速成像技术加速图像的采集,但可以降低图像质量.
适应性CS-Net在非对比3D全心REACT成像中的有效性尚不确定.

研究的目的:

评估适应性CS-Net在非对比3D全心REACT成像中的有效性.
为了比较适应性CS-Net重建的REACT图像,传统的CS重建的REACT图像和标准的3DbSSFP序列之间的图像质量和容器测量.

主要方法:

30名参与者接受了非对比修饰的REACT和标准的3DbSSFP序列.
REACT数据采集了六倍的亚样本,并使用传统的CS和自适应的CS-Net重建.
图像质量和船体截面面积被评估并使用弗里德曼和恩的后期测试进行统计比较.

主要成果:

与3D bSSFP相比,自适应的CS-Net和CS重建的REACT图像显示了肺静脉,部和上胸血管的优越图像质量.
这两种REACT重建方法的肺静脉,上升性大动脉和上腔静脉对比3DbSSFP的对比度 (CNR) 显著更高 (p <0.05).
适应式CS-Net重建显示图像质量与传统CS相比相当或优越.

结论:

对REACT图像的自适应CS-Net重建始终为传统CS提供优越或可比的图像质量.
与标准的3D bSSFP相比,自适应的CS-Net显著提高了肺静脉,部和上胸血管的图像质量.
这种深度学习方法为快速,高质量的非对比心血管磁共振成像提供了有希望的进步.

关键词:

适应性CS-Net是可以调整的.人工智能的人工智能是人工智能.心血管磁共振是一种心血管磁共振.遗传性心脏病是一种先天性心脏病.深度学习是一种深度学习.

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