  Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical
structures. However, the acquisition of MR volumes with high spatial resolution
leads to long scan times. To this end, we propose volumetric super-resolution
forests (VSRF) to enhance MRI resolution retrospectively. Our method learns a
locally linear mapping between low-resolution and high-resolution volumetric
image patches by employing random forest regression. We customize features
suitable for volumetric MRI to train the random forest and propose a median
tree ensemble for robust regression. VSRF outperforms state-of-the-art
example-based super-resolution in term of image quality and efficiency for
model training and inference in different MRI datasets. It is also superior to
unsupervised methods with just a handful or even a single volume to assemble
training data.
