从常规心脏MRI研究中完全自动测量大动脉脉冲波速
Yue Jiang1, Tina Yao1, Nikhil Paliwal1
1Institute of Cardiovascular Science, University College London, London WC1N 1DZ, United Kingdom.
Magnetic resonance imaging
|June 1, 2025
概括
深度学习完全自动化了从标准MRI扫描中对大动脉脉冲波速度 (PWV) 的测量. 这种方法准确评估心血管疾病风险,使大规模的临床和人口研究成为可能.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
- 医学图像分析 医学图像分析
背景情况:
- 大动脉脉冲波速度 (PWV) 是心血管疾病的关键预后生物标志物.
- 目前用于PWV测量的MRI方法受到专业序列和手动分析的限制.
- 从标准MRI序列自动化PWV测量对于临床和研究应用至关重要.
研究的目的:
- 开发和验证一种完全自动化的深度学习 (DL) 方法,用于使用标准心脏MRI序列测量PWV.
- 在常规临床实践和大规模研究中促进有效和准确的PWV评估.
主要方法:
- 创建了一个DL模型,从2DSSFP定位器图像中对大动脉进行细分,用于路径长度估计.
- 另一种DL模型在相对比图像中对大动脉进行了细分,以估计脉冲过渡时间.
- 自动化方法与使用临床数据集和英国生物库对象的训练有素观察员进行了定量比较.
主要成果:
- 与手动测量相比,自动化大动脉通路长度测量没有显著差异 (125 ± 19毫米与124 ± 19毫米相比,P=0.88).
- 在相对比图像中,自动细分上升式和下降式大动脉实现了高准确度 (分别为0.96和0.89的迪斯分数).
- 在自动化方法和手动分析 (21 ± 9 ms vs. 22 ± 9 ms,P=0.15) 之间,脉冲过渡时间估计是可比的.
- 该方法在1053名英国生物库受试者中成功应用于92%的受试者,揭示了与PWV的年龄和血压关联.
结论:
- 从标准MRI序列 (局部化器和相对比) 进行PWV测量的全自动DL方法已经开发出来.
- 该方法强大,准确,适合常规的临床扫描和大规模的人口研究.
- DL模型和部署代码是公开的,促进了更广泛的采用和研究.
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