Magnetic resonance imaging (MRI) is widely used for screening, diagnosis,
image-guided therapy, and scientific research. A significant advantage of MRI
over other imaging modalities such as computed tomography (CT) and nuclear
imaging is that it clearly shows soft tissues in multi-contrasts. Compared with
other medical image super-resolution (SR) methods that are in a single
contrast, multi-contrast super-resolution studies can synergize multiple
contrast images to achieve better super-resolution results. In this paper, we
propose a one-level non-progressive neural network for low up-sampling
multi-contrast super-resolution and a two-level progressive network for high
up-sampling multi-contrast super-resolution. Multi-contrast information is
combined in high-level feature space. Our experimental results demonstrate that
the proposed networks can produce MRI super-resolution images with good image
quality and outperform other multi-contrast super-resolution methods in terms
of structural similarity and peak signal-to-noise ratio. Also, the progressive
network produces a better SR image quality than the non-progressive network,
even if the original low-resolution images were highly down-sampled.

本文提出了一种用于 MRI 多对比度超分辨率图像的神经网络方法，实验结果表明该网络可以产生高质量的超分辨率图像，在图像质量和结构相似度方面优于其他多对比度超分辨率方法，而且渐进网络即使在原始低分辨率图像高度下采样时，也比非渐进网络更能产生更好的 SR 图像质量。