摘要
心血管疾病(cardiovascular diseases,CVDs)的高发病率和高死亡率已经严重影响了人类的生存质量.如何评估心脏功能、辅助临床CVDs诊疗和预后评估,是一个迫切需要解决的问题.针对这个问题,本文在前期心脏电影磁共振(cardiac cine magnetic resonance,CCMR)图像左心肌分割的基础上,提出一种基于位移流U-Net(DispFlow_UNet)和生物力学变分自动编码器(variational autoencoder,VAE)的左心肌运动追踪方法:DispFlow_UNet_VAE.主要研究内容有:1)搭建压缩激励残差U-net网络精准分割左心肌,根据分割结果计算心室体积、心肌质量等,评估心脏整体功能;2)根据DispFlow_UNet_VAE估计CCMR图像连续帧之间的左心室运动,结合左心肌分割掩膜得到左心肌密集位移场;3)利用模拟数据真实位移场、临床数据集对追踪结果进行对比和评估.结果表明,本文追踪算法具有较高的精度和泛化能力.
The high morbidity and mortality of cardiovascular diseases(CVDs)seriously affects the quality of human life.How to asses cardiac function,assist in the diagnosis and treatment of clinical CVDs and evaluate prognosis is a problem to be solved urgently.In response to this issue,based on previous work of Cardiac Cine Magnetic Resonance(CCMR)image segmentation of the left myocardium(LVM),a robust and accurate LVM motion tracking method(DispFlow_UNet_Flow)with using the displacement flow UNet(DispFlow_UNet)and biomechanics-informed variational autoencoder(VAE)is proposed in this paper.The following are the main research contents:(1)building a compressed excitation residual U-net network to accurately segment LVM,calculating the ventricular volume and myocardial mass according to the segmentation results,and then evaluating the overall cardiac function;(2)reconstructing the dense displacement field(DDF)based on the proposed motion tracking method,and obtaining the LVM dense displacement field by combining the LVM segmentation mask;(3)contrasting and evaluating the motion tracking results by using the true displacement vector field of simulated data and clinical data sets.All the results show that the tracking algorithm proposed in this paper has high precision and generalization capability.
作者
王甜甜
王慧
朱艳春
王丽嘉
Wang Tian-Tian;Wang Hui;Zhu Yan-Chun;Wang Li-Jia(School of Medical Instrument and Food Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Guanzhou Life Science Innovation Center,China Unicom Medical Base,Guangzhou 510000,China)
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2021年第22期319-329,共11页
Acta Physica Sinica
基金
广东省重点领域研发计划项目(批准号:2019B20230004,2020B010113015)资助的课题.
关键词
左心肌追踪
深度学习
位移流
U-Net
变分自动编码器
left myocardium tracking
deep learning
displacement flow U-Net network
variational autoencoder
作者简介
通信作者:王丽嘉.E-mail:lijiawangmri@163.com。
