Super-Resolution for Ultra High-Field MR Images

Wang, Q; Steiglechner, J; Lindig, T; Bender, B; Scheffler, K; Lohmann, G

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Super-Resolution for Ultra High-Field MR Images

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Wang, Q
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Steiglechner, J
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Lindig, T
Institutional Guests, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Scheffler, K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Lohmann, G
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://openreview.net/pdf?id=EFiFV2MSNEB
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Citation

Wang, Q., Steiglechner, J., Lindig, T., Bender, B., Scheffler, K., & Lohmann, G. (2022). Super-Resolution for Ultra High-Field MR Images. In Medical Imaging with Deep Learning (MIDL 2022).

Cite as: https://hdl.handle.net/21.11116/0000-000A-633D-3

Abstract

Segmenting ultra high-field MR images is an important first step in many applications. Segmentation methods based on machine learning have been shown to be valuable tools for this purpose. However, for ultra high-field MR images ( 7 Tesla), a lack of training data is a problem. Therefore, in this work, we propose to use super-resolution for augmenting the training set. Specifically, we describe an efficient super-resolution model based on Generative Adversarial Network(GAN). It produces synthetic images that simulate MR data at ultra high isotropic resolutions of mm. We present the first results that show an improvement in segmentation accuracy of imaging data acquired at a 9.4 Tesla MRI scanner.