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加速心脏MRI与基于深度学习的图像重建用于电影成像

Ann-Christin Klemenz¹, Linda Reichardt¹, Margarita Gorodezky¹

¹From the Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology (A.C.K., L.R., M.M., A.D., M.A.W., F.G.M.), and Department of Cardiology (C.I.L.), Rostock University Medical Center, Schillingallee 36, 18057 Rostock, Germany; GE HealthCare, Munich, Germany (M.G.); GE HealthCare, Menlo Park, Calif (X.Z.); and Department of Radiology, Ludwig-Maximilian University, Munich, Germany (R.L.).

Radiology. Cardiothoracic imaging

|November 14, 2024

概括

深度学习 (DL) 心脏MRI显著减少扫描时间. 三节拍采集提供了速度和质量的最佳平衡,与标准方法相匹配,而不会影响结果.

科学领域:

心血管成像 - 心血管成像
医学物理医学物理
人工智能在医学中的应用

背景情况:

心脏MRI (磁共振成像) 对于评估心脏功能至关重要.
传统的电影序列可能会很长,这会影响患者的舒适度和吞吐量.
深度学习 (DL) 提供了加速MRI获取和重建的潜力.

研究的目的:

评估基于DL的重建对心脏MRI电影序列的影响.
将DL电影序列的获取时间,图像质量和体积精度与标准方法进行比较.
为了确定最佳的DL cine获取策略 (1,3,或6个心脏周期).

主要方法:

一项前性研究,涉及55名健康志愿者接受1.5T心脏MRI.
获得了DL电影序列 (1,3,6个心脏周期) 和标准电影序列 (10-12个周期).
评估的参数包括采集时间,主观图像质量,边缘清晰度和左心室喷射率 (LVEF).

主要成果:

DL电影序列显著减少了采集时间:47秒 (1RR),108秒 (3RR),184秒 (6RR) 和227秒 (标准).
对于标准,3RR和6RR DL电影序列 (分别为63%,61%,62%的中位数),LVEF是可比的.
1RR DL cine低估了LVEF (57%); 3RR DL cine显示出与标准序列相似的图像质量和清晰度.

结论:

基于深度学习的电影序列显著缩短了心脏MRI获取时间.
三个心脏周期的DL获取提供了最佳的平衡,匹配标准序列的图像质量和体积精度.
与传统方法相比,这种方法可以节省50%以上的时间,而不会影响诊断效用.

关键词:

加速成像是一种加速成像技术.一个心脏病患者的心脏病.心脏磁力共振成像 (MRI)临床成像技术临床成像技术深度学习 (Deep Learning) 是一种深度学习.心灵的心脏心脏的心脏心脏心脏在MR成像中使用MR成像.技术方面技术方面

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