  There is a growing demand for high-resolution (HR) medical images in both the
clinical and research applications. Image quality is inevitably traded off with
the acquisition time for better patient comfort, lower examination costs, dose,
and fewer motion-induced artifacts. For many image-based tasks, increasing the
apparent resolution in the perpendicular plane to produce multi-planar
reformats or 3D images is commonly used. Single image super-resolution (SR) is
a promising technique to provide HR images based on unsupervised learning to
increase resolution of a 2D image, but there are few reports on 3D SR. Further,
perceptual loss is proposed in the literature to better capture the textual
details and edges than using pixel-wise loss functions, by comparing the
semantic distances in the high-dimensional feature space of a pre-trained 2D
network (e.g., VGG). However, it is not clear how one should generalize it to
3D medical images, and the attendant implications are still unclear. In this
paper, we propose a framework called SOUP-GAN: Super-resolution Optimized Using
Perceptual-tuned Generative Adversarial Network (GAN), in order to produce
thinner slice (e.g., high resolution in the 'Z' plane) medical images with
anti-aliasing and deblurring. The proposed method outperforms other
conventional resolution-enhancement methods and previous SR work on medical
images upon both qualitative and quantitative comparisons. Specifically, we
examine the model in terms of its generalization for various SR ratios and
imaging modalities. By addressing those limitations, our model shows promise as
a novel 3D SR interpolation technique, providing potential applications in both
clinical and research settings.
