Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.
Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.
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DOI：
10.1109/tmi.2018.2858752
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发表时间：
2019-01
影响因子：
10.6
通讯作者：
Pauly JM
中科院分区：
文献类型：
--
作者：
Mardani M;Gong E;Cheng JY;Vasanawala SS;Zaharchuk G;Xing L;Pauly JM
欠采样磁共振图像（MRI）重建是一个典型的不适定线性逆任务。时间和资源密集型计算需要在精度和速度之间进行权衡。此外，最先进的压缩传感（CS）分析不认识图像诊断质量。为了解决这些挑战，我们提出了一种新的CS框架，该框架使用生成对抗网络（GAN）来对高质量MR图像的（低维）流形进行建模。利用最小二乘（LS）GAN和像素级的GAN 1/GAN 2成本的混合，具有跳过连接的深度残差网络被训练为生成器，该生成器通过投影到图像流形上来学习去除混叠伪影。LSGAN学习纹理细节，而101/102成本抑制高频噪声。作为多层卷积神经网络（CNN）的神经网络起着感知成本的作用，然后基于高质量MR图像进行联合训练，以对检索到的图像的质量进行评分。在操作阶段，初始混叠估计（例如，简单地通过零填充获得）被传播到训练的生成器中以输出期望的重建。这需要非常低的计算开销。对儿科患者的大型对比增强MR数据集进行了广泛的评价。由放射科专家评定的图像证实，与传统的基于小波和基于字典学习的CS方案以及使用逐像素训练的基于深度学习的方案相比，GANCS检索更高质量的图像，具有改进的精细纹理细节。此外，它提供了几毫秒以下的重建时间，比目前最先进的CS-MRI方案快两个数量级。
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require trade offs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative adversarial networks (GAN) to model the (low-dimensional) manifold of high-quality MR images. Leveraging a mixture of least-squares (LS) GANs and pixel-wise ℓ1/ℓ2 cost, a deep residual network with skip connections is trained as the generator that learns to remove the aliasing artifacts by projecting onto the image manifold. The LSGAN learns the texture details, while the ℓ1/ℓ2 cost suppresses high-frequency noise. A discriminator network, which is a multilayer convolutional neural network (CNN), plays the role of a perceptual cost that is then jointly trained based on high quality MR images to score the quality of retrieved images. In the operational phase, an initial aliased estimate (e.g., simply obtained by zero-filling) is propagated into the trained generator to output the desired reconstruction. This demands very low computational overhead. Extensive evaluations are performed on a large contrast-enhanced MR dataset of pediatric patients. Images rated by expert radiologists corroborate that GANCS retrieves higher quality images with improved fine texture details compared with conventional Wavelet-based and dictionary-learning based CS schemes as well as with deep-learning based schemes using pixel-wise training. In addition, it offers reconstruction times of under a few milliseconds, which is two orders of magnitude faster than current state-of-the-art CS-MRI schemes.