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.
翻译:获取高分辨率磁共振图像是一个耗时的过程,这使得它不适合医疗紧急情况和儿科病人。相形之下,低分辨率的MR成像比高分辨率的成像更快,但它在更精确的诊断所需的细细细节上有所妥协。超分辨率(SR)在应用到低分辨率的MR图像时,可以通过合成生成高分辨率图像来增加其实用性,而无需多花一点时间。在本文中,我们介绍了基于基因对抗网络(GANs)的MR图像的SR技术。 事实证明,这非常有助于在SR产生直观的细节。我们引入了一种有条件的GAN,带有感官损失,以输入的低分辨率图像为条件,这些图像改善了异形和异粒状磁共振超分辨率的性能。