Super-resolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks
Super-resolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks
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DOI：
10.1016/j.compmedimag.2020.101698
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发表时间：
2020-03-01
影响因子：
5.7
通讯作者：
Wong, Kelvin K. L.
中科院分区：
文献类型：
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作者：
Zhao, Ming;Liu, Xinhong;Wong, Kelvin K. L.
背景与目的：心脏磁共振成像（MRI）可以辅助心脏的功能和结构分析，但由于硬件和物理限制，高分辨率MRI扫描耗时且峰值信噪比（PSNR）较低。现有的超分辨率方法试图解决这一问题，但仍存在超分辨率后细节产生幻觉、重建后精度低等缺点。为了解决这些问题，我们提出了拉普拉斯金字塔生成对抗网络（LSRGAN）来生成视觉上更好的心血管超声图像，以帮助医生诊断和治疗。方法与结果：为了解决图像分辨率低的问题，采用拉普拉斯金字塔分析了不同像素大小图像的超分辨率（SR）重建的高频细节特征。为了消除梯度消失，我们实现了最小二乘损失函数作为鉴别器，引入残差密集块（RDB）作为基本网络构建单元，用于生成更高质量的图像。实验结果表明，LSRGAN可以有效避免超分辨率后的错觉细节，具有较好的重建质量。与最先进的方法相比，我们提出的算法产生更高质量的超分辨率图像，具有更高的峰值信噪比和结构相似性（SSIM）分数。结论：我们实现了一种新颖的LSRGAN网络模型，该模型解决了MRI超分辨率后分辨率不足和幻觉细节的问题。我们的研究为医学专家诊断和治疗心肌缺血和心肌梗死提供了一种优越的超分辨率方法。(C) 2020由Elsevier Ltd.出版。
Background and objective: Cardiac magnetic resonance imaging (MRI) can assist in both functional and structural analysis of the heart, but due to hardware and physical limitations, high-resolution MRI scans is time consuming and peak signal-to-noise ratio (PSNR) is low. The existing super-resolution methods attempt to resolve this issue, but there are still shortcomings, such as hallucinate details after super-resolution, low precision after reconstruction, etc. To dispose these problems, we propose the Laplacian Pyramid Generation Adversarial Network (LSRGAN) in order to generate visually better cardiovascular ultrasound images so as to aid physician diagnosis and treatment.Methods and results: In order to address the problem of low image resolution, we used the Laplacian Pyramid to analyze the high-frequency detail features of super-resolution (SR) reconstruction of images with different pixel sizes. To eliminate gradient disappearance, we implemented the least squares loss function as the discriminator, we introduce the residual-dense block (RDB) as the basic network building unit is used to generate higher quality images. The experimental results show that the LSRGAN can effectively avoid the illusion details after super-resolution and has the best reconstruction quality. Compared with the state-of-the-art methods, our proposed algorithm generates higher quality super-resolution images that comes with higher peak signal-to-noise ratio and structural similarity (SSIM) scores.Conclusion: We implemented a novel LSRGAN network model, which solves reduces insufficient resolution and hallucinate details of MRI after super-resolution. Our research presents a superior super-resolution method for medical experts to diagnose and treat myocardial ischemia and myocardial infarction. (C) 2020 Published by Elsevier Ltd.