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

Sindel A, Breininger K, Käßer J, Heß A, Maier A, Köhler T (2018)

Publication Status: Published

Publication Type: Conference contribution, Conference Contribution

Publication year: 2018

Publisher: IEEE Computer Society

Pages Range: 1453-1457

Article Number: 8451320

ISBN: 9781479970612

URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Sindel18-LFA.pdf

DOI: 10.1109/ICIP.2018.8451320

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 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.

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How to cite

APA:

Sindel, A., Breininger, K., Käßer, J., Heß, A., Maier, A., & Köhler, T. (2018). Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests. In Proceedings of the 25th IEEE International Conference on Image Processing, ICIP 2018 (pp. 1453-1457). IEEE Computer Society.

MLA:

Sindel, Aline, et al. "Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests." Proceedings of the 25th IEEE International Conference on Image Processing, ICIP 2018 IEEE Computer Society, 2018. 1453-1457.

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