一个运动校正的深度学习重建框架,用于加速全心磁共振成像在患有先天性心脏病的患者
Andrew Phair1, Anastasia Fotaki1, Lina Felsner1
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
概括
深度学习重建加速了先天性心脏病 (CHD) 患者的3D全心 MRI. MoCo-MoDL框架在显著更快的扫描和重建时间中实现了可比的图像质量.
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) 对于成人先天性心脏病 (CHD) 管理至关重要.
- 传统的3D全心MRI扫描是漫长的,并具有不可预测的获取时间.
- 加快的MRI技术往往需要长时间的代重建.
研究的目的:
- 适应和验证一个深度学习框架 (MoCo-MoDL) 的加速3D全心MRI.
- 评估该框架在患有心脏病的成年患者中的性能.
- 将重建速度和图像质量与现有方法进行比较.
主要方法:
- 集成非刚性运动校正和无声化的MoCo-MoDL框架在39个CHD数据集上进行了训练.
- 经过训练的框架被评估,使用了来自八名心血管疾病患者的前性七倍低样本数据.
- 重建质量与最先进的NR-PROST和参考标准图像进行了比较.
主要成果:
- 七倍低样本扫描时间为2.1±0.3分钟,重建时间为~30秒.
- 这与NR-PROST重建相比,代表了约240倍的加速.
- MoCo-MoDL实现了与参考图像相当的图像质量,专家评分偏好了MoCo-MoDL而不是NR-PROST.
结论:
- MoCo-MoDL框架成功地为成年心脏病患者提供高质量的3D全心磁共振成像.
- 该方法可实现快速成像,扫描时间约为2分钟,重建时间约为30秒.
- 这种深度学习方法显著提高了心血管磁共振用于心血管疾病评估的效率.
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