用于SAR估计的基于U-Net网络的快速膝关节模型重建

doi:10.11938/cjmr20192716

波谱学杂志 ›› 2020, Vol. 37 ›› Issue (2): 144-151.doi: 10.11938/cjmr20192716

用于SAR估计的基于U-Net网络的快速膝关节模型重建

肖亮, 娄煜堃, 周航宇

北京化工大学信息科学与技术学院, 北京 100029

收稿日期:2019-02-27 出版日期:2020-06-05 发布日期:2019-05-30
通讯作者: 肖亮,Tel:010-64414931,E-mail:xiaoliang@mail.buct.edu.cn. E-mail:xiaoliang@mail.buct.edu.cn
基金资助:
北京化工大学高精尖科技创新团队基金资助项目（buctylkjcx06）.

A U-Net Network-Based Rapid Construction of Knee Models for Specific Absorption Rate Estimation

XIAO Liang, LOU Yu-kun, ZHOU Hang-yu

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Received:2019-02-27 Online:2020-06-05 Published:2019-05-30

摘要/Abstract

摘要： 膝关节高场磁共振成像（MRI）时，射频功率沉积（SAR）是一个关键的安全指标.目前对于局部SAR的准确估计只能通过电磁仿真实现，这就要求得到每一个个体的膝关节模型.本文提出一种针对低场磁共振图像的基于卷积神经网络的分割方法，以实现膝关节磁共振图像的快速重建.数据集来自于矢位T₁加权自旋回波图像，将膝关节组织按照"肌肉-脂肪-骨骼"模型进行简化，除脂肪与骨骼之外的其他组织归类为肌肉.采用一种全卷积的神经网络，即U-Net进行逐层的图像分割，卷积层数为4，训练采用交叉熵函数.本文对图像的自动分割结果与手动标注结果进行了定量的比较.此外，采用3 T正交鸟笼线圈进行了SAR仿真，结果验证了组织简化对于SAR估计的可行性，并且所提方法构建的模型可以得到较为精准的局部SAR分布.

关键词: 高场磁共振成像, 射频功率沉积(SAR), 膝关节, U-Net网络, 图像分割

Abstract: The specific absorption rate (SAR) needs to be estimated for safety concerns when performing high-field magnetic resonance imaging (MRI) on knee joint. Electromagnetic simulation can be used to calculate the local SAR on knees if a patient-specific knee model is available. In this work, a method for rapid construction of knee models from low-field magnetic resonance knee images was proposed. A convolutional neural network (CNN) was first used to segment the sagittal T₁-weighted spin echo images of knees into fat, bone and muscle. All the pixels other than those from the fat and bone are classified as muscle pixels. The U-Net network, a full CNN with a convolutional layer number of four and adopting a cross entropy function, was used to perform segmentation slice-by-slice. The results from automatic segmentation were compared with those obtained with manual delineation with quantitative measures. Moreover, SAR with a 3 T quadrature birdcage coil was calculated. The simulation results validated the proposed method by showing that and a relatively accurate local SAR estimation could be obtained with the knee models constructed from low-field knee images.

Key words: high-field magnetic resonance imaging (MRI), specific absorption rate (SAR), knee joint, U-Net network, image segmentation

中图分类号:

肖亮, 娄煜堃, 周航宇. 用于SAR估计的基于U-Net网络的快速膝关节模型重建[J]. 波谱学杂志, 2020, 37(2): 144-151.

XIAO Liang, LOU Yu-kun, ZHOU Hang-yu. A U-Net Network-Based Rapid Construction of Knee Models for Specific Absorption Rate Estimation[J]. Chinese Journal of Magnetic Resonance, 2020, 37(2): 144-151.

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用于SAR估计的基于U-Net网络的快速膝关节模型重建

A U-Net Network-Based Rapid Construction of Knee Models for Specific Absorption Rate Estimation

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