[1]弥佳,周宇佳,冯前进.基于生成对抗网络的无监督动态对比增强磁共振分解方法[J].中国医学物理学杂志,2021,38(2):186-192.[doi:DOI:10.3969/j.issn.1005-202X.2021.02.011]
　MI Jia,ZHOU Yujia,FENG Qianjin.Unsupervised image decomposition of DCE-MRI based on generative adversarial networks[J].Chinese Journal of Medical Physics,2021,38(2):186-192.[doi:DOI:10.3969/j.issn.1005-202X.2021.02.011]
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基于生成对抗网络的无监督动态对比增强磁共振分解方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第2期
页码:
186-192
栏目:
医学影像物理
出版日期:
2021-02-02

文章信息/Info

Title:
Unsupervised image decomposition of DCE-MRI based on generative adversarial networks
文章编号:
1005-202X（2021）02-0186-07
作者:
弥佳; 周宇佳; 冯前进
南方医科大学生物医学工程学院/广东省医学图像重点实验室， 广东 广州510515
Author(s):
MI Jia;  ZHOU Yujia;  FENG Qianjin
School of Biomedical Engineering, Southern Medical University/Key Laboratory for Medical Image Processing in Guangdong Province, Guangzhou 510515, China
关键词:
深度学习; 生成对抗网络; 图像分解; 动态对比增强磁共振
Keywords:
Keywords: deep leaning generative adversarial network image decomposition dynamic contrast-enhanced magnetic resonance imaging
分类号:
R318；TP183
DOI:
DOI:10.3969/j.issn.1005-202X.2021.02.011
文献标志码:
A
摘要:
为排除动态对比增强磁共振（DCE-MRI）中造影剂对图像定量分析的影响，可以使用图像分解的方法，将图像中造影剂造成的灰度变化分解出去，并保持其他内容不变。本文提出一种基于生成对抗网络的无监督图像分解方法，以实现对DCE-MRI中造影剂造成的灰度变化的分解。该方法假设增强图像由增强成分和解剖成分组成，基于对各成分复杂度的先验设计生成器，通过双路生成网络，同时但独立地提取解剖成分和增强成分的信息。同时，引入图像重构损失作为反馈信号来约束生成。在MNIST数据集上基于MSE、SSIM、PSNR与现有的方法进行对比，验证了该方法的有效性。并在临床的肝部DCE-MRI数据进行了实验。实验表明该方法可以在没有成对训练数据的情况下有效实现DCE-MRI图像分解。
Abstract:
Abstract: Image decomposition can be used to separate the grayscale variations caused by contrast agent from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) image, with other contents unchanged, thereby eliminating the effects of contrast agent on quantitative analysis. An unsupervised image decomposition method based on generative adversarial networks is proposed to achieve the separation of grayscale variations caused by contrast agent in DCE-MRI. The method assumes that the enhanced image is composed of contrast component and anatomy component, and two generators are designed based on the complexity of each component. Through the double-generator, the information of anatomy component and contrast component can be extracted simultaneously and independently. Meanwhile, the image reconstruction loss is introduced as a feedback signal to constrain the generation. Mean-square error, peak signal to noise ratio and structural similarity index are used to compare the proposed method with the existing methods on MNIST dataset, thus verifying the effectiveness of the proposed method. Moreover, some experiments are carried out on clinical liver DCE-MRI data, and the experimental results show that the proposed method can be used to effectively achieve DCE-MRI image decomposition without paired training data.

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备注/Memo

备注/Memo:
【收稿日期】2020-10-02 【基金项目】国家自然科学基金（81801780，81974275） 【作者简介】弥佳，在读硕士，研究方向：医学图像分析与处理，E-mail: 2195775838@qq.com 【通信作者】冯前进，教授，博士生导师，主要研究方向：医学成像与图像分析，E-mail: qianjinfeng08@gmail.com
更新日期/Last Update: 2021-02-02