Super-resolution (SR) for image enhancement has great importance in medical
image applications. Broadly speaking, there are two types of SR, one requires
multiple low resolution (LR) images from different views of the same object to
be reconstructed to the high resolution (HR) output, and the other one relies
on the learning from a large amount of training datasets, i.e., LR-HR pairs. In
real clinical environment, acquiring images from multi-views is expensive and
sometimes infeasible. In this paper, we present a novel Generative Adversarial
Networks (GAN) based learning framework to achieve SR from its LR version. By
performing simulation based studies on the Multimodal Brain Tumor Segmentation
Challenge (BraTS) datasets, we demonstrate the efficacy of our method in
application of brain tumor MRI enhancement. Compared to bilinear interpolation
and other state-of-the-art SR methods, our model is lesion focused, which is
not only resulted in better perceptual image quality without blurring, but also
more efficient and directly benefit for the following clinical tasks, e.g.,
lesion detection and abnormality enhancement. Therefore, we can envisage the
application of our SR method to boost image spatial resolution while
maintaining crucial diagnostic information for further clinical tasks.Comment: 4 pages, 2 figures, 1 table, Accepted as Oral Presentation by the
SPIE Medical Imaging Conference 201
