WarpPINN:使用物理信息的神经网络进行Cine-MR图像注册
Pablo Arratia López1, Hernán Mella2, Sergio Uribe3
1Department of Mathematical Sciences, University of Bath, Bath, UK.
Medical image analysis
|August 20, 2023
概括
一个新的物理信息神经网络WarpPINN精确地从MRI扫描中量化了局部心脏变形. 该方法通过提供超出全球功能评估的详细应变分析,提高了心力衰竭的诊断.
科学领域:
- 医疗成像医学成像
- 生物物理学的生物物理.
- 机器学习 机器学习
背景情况:
- 目前的心力衰竭诊断依赖于全球功能评估,如喷射分数,这些评估对各种心肌病缺乏特异性.
- 量化局部心脏变形 (应变) 提供了有价值的诊断信息,但也带来了重大技术挑战.
研究的目的:
- 介绍WarpPINN,一个基于物理的神经网络,用于准确的心脏图像记录和局部变形测量.
- 使用电影MRI数据来估计心脏运动和心脏周期中的应变.
主要方法:
- 开发了WarpPINN,一个神经网络,通过雅科比惩罚将组织几乎无压缩性纳入.
- 采用损失函数,结合基于强度的相似性和超弹性组织行为的规范化.
- 集成的福里埃特征映射,以减轻神经网络的光谱偏差,并捕捉应变场的不连续性.
主要成果:
- WarpPINN准确地估计心脏运动,并提供辐射和周长方向的生理应变测量.
- 该方法在精度上优于现有的地标跟踪技术.
- 在合成数据和15名健康志愿者的cine SSFP MRI扫描的基准上证明了有效性.
结论:
- WarpPINN可实现精确的局部心脏变形测量,改善心力衰竭诊断.
- 开发的物理信息神经网络适用于一般医疗图像记录任务.
- 精确的菌株量化有助于区分影响区域心脏功能的心肌病.
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