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为快速心血管磁共振成像协议进行深度学习重建:与传统心血管磁共振进行比较研究

Yiying Hua¹, Hongfei Lu¹, Shiya Wang¹

¹Department of Radiology, Zhongshan Hospital, Fudan University and Shanghai Institute of Medical Imaging, Shanghai, China.

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

|November 29, 2025

概括

深度学习 (DL) 重建显著减少了心血管磁共振 (CMR) 采集时间的57%,提高了图像质量. 这种人工智能驱动的方法保持了诊断准确性,支持其融入临床实践.

科学领域:

心血管磁共振 (CMR) 影像成像
医疗成像中的人工智能
深度学习 (DL) 应用程序

背景情况:

虽然CMR是心脏诊断的黄金标准,但由于长时间的扫描时间而受到限制.
深度学习 (DL) 显示出加速CMR获取的前景.
需要对多个CMR序列进行DL的前性验证.

研究的目的:

评估压缩感应人工智能 (CSAI) 算法,以加速CMR获取.
与SENSE重建相比,评估CSAI对图像质量的影响.
为了确定CSAI是否在各种CMR序列中保持诊断准确性.

主要方法:

105名参与者接受了SENSE和CSAI加速的CMR序列 (Cine,T2 STIR,LGE).
主观图像质量使用5分利克特尺度进行评估.
定量指标包括SNR,CNR,边缘度和特定序列的参数.

主要成果:

在CSAI中,获取时间减少了57.4% (159.2s vs 277.1s; P < 0.001).
与SENSE (P < 0.001) 相比,CSAI表现出更优异的主观得分 (Cine,T2 STIR) 和定量指标 (SNR,CNR,边缘度).
功能参数 (T2 SI比率,LGE百分比) 在CSAI和SENSE之间是可比的 (P > 0.05).

结论:

基于DL的CSAI重建显著加速CMR采集并提高图像质量.
在Cine,T2 STIR和LGE序列中,CSAI保持了诊断准确性和定量一致性.
这些发现支持DL加速CMR工作流程的临床整合.

关键词:

人工智能的人工智能是人工智能.心血管疾病的心血管疾病深度学习 (Deep Learning) 是一种深度学习.磁共振成像技术磁共振成像技术

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