基于MRI深度学习的自动隔膜隔膜对黑血心肌T2*测量的隔膜隔膜细分,用于thalassemia的测量
Zifeng Lian1,2,3, Qiqi Lu1,2,3, Bingquan Lin4
1School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Journal of magnetic resonance imaging : JMRI
|November 9, 2023
概括
深度学习准确地对心肌T2*测量中心室间隔膜进行细分,提高了血病患者的效率并减少了铁负载评估的变化.
科学领域:
- 心血管磁共振成像 - 心血管磁共振成像
- 医疗成像中的人工智能
- 血液学 血液学 血液学
背景情况:
- 在thalassemia major中,心肌铁过载是使用T2*值评估的.
- 自动隔膜细分可以减少分析时间和观察者之间的变化.
研究的目的:
- 开发和验证一个深度学习方法,用于自动间静脉隔膜细分.
- 为了使心肌T2*测量准确的thalassemia患者使用心脏MRI.
主要方法:
- 为隔膜细分开发了一种修改的注意力U-Net模型.
- 该模型在两个中心 (1.5T和3.0T) 的146名沙拉西米亚患者的心脏MRI数据上进行了训练和测试.
- 该协议的评估是使用Dice系数进行细分和Bland-Altman地块/T2*值的变化系数进行评估.
主要成果:
- 深度学习模型在内部和外部数据集 (1.5T和3.0T) 中实现了高细分精度 (中位数:0.82-0.90).
- 自动细分导致T2*测量与手动细分高度一致 (平均差异:0.02-0.43毫秒).
- 方法之间的T2*测量变化系数很低 (3.1%-7.0%).
结论:
- 拟议的基于深度学习的自动隔膜细分提供可靠的心肌T2*测量.
- 这种自动化方法可以简化心脏MRI分析的thalassemia患者.
- 它有潜力减少临床实践中的依赖操作者的变异性.
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