Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests - Sindel, Aline; Breininger, Katharina; Käßer, Johannes; Heß, Andreas; Maier, Andreas; Köhler, Thomas (25th IEEE International Conference on Image Processing, ICIP 2018, 2018)
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 terms of image quality and efficiency for model training and inference on different MRI datasets. It is also superior to unsupervised methods with just a handful or even a single volume to assemble training data.<br
Keywords: MRI; Super-resolution; Random forests