Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks
Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks
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DOI：
10.1109/tmi.2019.2901750
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发表时间：
2019-10-01
影响因子：
10.6
通讯作者：
Cukur, Tolga
中科院分区：
文献类型：
--
作者：
Dar, Salman U. H.;Yurt, Mahmut;Cukur, Tolga
获取具有多个不同对比的相同解剖结构的图像增加了MR检查中可用诊断信息的多样性。然而，扫描时间的限制可能会阻止某些对比度的获取，并且一些对比度可能会被噪声和伪影破坏。在这种情况下，合成未获得或损坏对比的能力可以提高诊断的实用性。对于多对比度综合，目前的方法是通过非线性回归或确定性神经网络学习源图像和目标图像之间的非线性强度变换。反过来，这些方法可能会损失合成图像中的结构细节。在本文中，我们提出了一种基于条件生成对抗网络的多对比MRI合成新方法。该方法通过对抗损失保留了中高频率的细节，并通过对注册的多对比度图像的像素和感知损失以及对未注册的图像的周期一致性损失提供了增强的合成性能。利用相邻截面的信息进一步提高合成质量。来自健康受试者和患者的T-1和T-2加权图像的演示清楚地表明，与先前最先进的方法相比，所提出的方法具有优越的性能。我们的综合方法可以帮助提高多对比MRI检查的质量和通用性，而不需要长时间或重复检查。
Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. For multi-contrast synthesis, the current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks. These methods can, in turn, suffer from the loss of structural details in synthesized images. Here, in this paper, we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. The proposed approach preserves intermediate-to-high frequency details via an adversarial loss, and it offers enhanced synthesis performance via pixel-wise and perceptual losses for registered multi-contrast images and a cycle-consistency loss for unregistered images. Information from neighboring cross-sections are utilized to further improve synthesis quality. Demonstrations on T-1 - and T-2 - weighted images from healthy subjects and patients clearly indicate the superior performance of the proposed approach compared to the previous state-of-the-art methods. Our synthesis approach can help improve the quality and versatility of the multi-contrast MRI exams without the need for prolonged or repeated examinations.