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.
@article{arxiv.1802.05518,
title = {Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests},
author = {Aline Sindel and Katharina Breininger and Johannes Käßer and Andreas Hess and Andreas Maier and Thomas Köhler},
journal= {arXiv preprint arXiv:1802.05518},
year = {2018}
}