论文标题
MRI超分辨率的感知CGAN
Perceptual cGAN for MRI Super-resolution
论文作者
论文摘要
捕获高分辨率磁共振（MR）图像是一个耗时的过程，这使其不适合医疗紧急情况和小儿患者。相比之下，低分辨率的MR成像比其高分辨率对应物更快，但它损害了更精确诊断所需的细节。当应用于低分辨率MR图像时，超分辨率（SR）可以通过合成生成高分辨率图像而又不额外的时间来帮助增加其效用。在本文中，我们为MR图像提供了一种SR技术，该技术基于生成的对抗网络（GAN），事实证明，该技术在SR中生成尖锐的细节非常有用。我们引入了一个有条件的gan，具有感知损失，该gan以输入低分辨率图像为条件，从而改善了各向同性和各向异性MRI超级分辨率的性能。
Capturing high-resolution magnetic resonance (MR) images is a time consuming process, which makes it unsuitable for medical emergencies and pediatric patients. Low-resolution MR imaging, by contrast, is faster than its high-resolution counterpart, but it compromises on fine details necessary for a more precise diagnosis. Super-resolution (SR), when applied to low-resolution MR images, can help increase their utility by synthetically generating high-resolution images with little additional time. In this paper, we present a SR technique for MR images that is based on generative adversarial networks (GANs), which have proven to be quite useful in generating sharp-looking details in SR. We introduce a conditional GAN with perceptual loss, which is conditioned upon the input low-resolution image, which improves the performance for isotropic and anisotropic MRI super-resolution.
