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arXiv · 1802.05518

Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests

Aline Sindel·Katharina Breininger·Johannes Käßer·Andreas Hess·Andreas Maier·Thomas Köhler
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Abstract

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.

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Aline Sindel, Katharina Breininger, Johannes Käßer, Andreas Hess, Andreas Maier, Thomas Köhler. 2018-02-15. Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests. https://arxiv.org/abs/1802.05518

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