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无参考4D流动心血管磁共振与深度学习

Chiara Trenti¹, Erik Ylipää², Tino Ebbers³

¹Department of Health, Medicine and Caring Sciences (HMV), Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping, Sweden.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance

|June 4, 2025

概括

深度学习通过预测参考编码,显著减少四维 (4D) 流动心血管磁共振 (CMR) 的扫描时间. 这使得在临床实践中更快,更高分辨率的心血管评估成为可能.

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:

四维 (4D) 流动心血管磁共振 (CMR) 对于评估心血管疾病至关重要,但受到长时间获取时间的限制.
传统的4D流CMR需要引用编码扫描,这有助于扩展扫描持续时间.
减少扫描时间对于改善4D流CMR的临床实用性和患者体验至关重要.

研究的目的:

开发和评估一种深度学习模型,用于预测4D流CMR中的参考编码.
为了实现无参考的4D流CMR采集,从而减少扫描时间,并可能提高图像分辨率.
与传统方法相比,评估基于深度学习的速度和流量量化的准确性.

主要方法:

使用对抗式学习 (U-Net_ADV) 和速度频率加权损失函数 (U-Net_VEL) 训练了一个U-Net深度学习架构.
这些模型预测了来自126名患者的全心4D流数据集中的三种运动编码的参考编码.
定量评估包括主要心脏结构中的流量,速度和动动能.

主要成果:

深度学习预测的参考编码产生了与扫描仪获取的数据相比的3D速度数据.
U-Net_ADV在整个心脏周期和受试者中表现一致,而U-Net_VEL在心缩速度预测方面表现出色.
流量量和流速的量化误差一般很低,在动动能计算中的特殊例外.

结论:

基于深度学习的无参考4D流量CMR提供了速度和流量量的准确量化.
消除参考扫描将获得的数据减少25%,从而允许缩短扫描时间或更高分辨率.
这一进步为4D流CMR的常规临床应用带来了巨大的潜力.

关键词:

4D 流量流程是什么?加速技术是一种加速技术.心血管成像 - 心血管成像深度学习是一种深度学习.阶段对比阶段对比阶段对比

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