Super-Resolution for Ultra High-Field MR Images

Qi Wang, Julius Steiglechner, Tobias Lindig, Benjamin Bender, Klaus Scheffler, Gabriele Lohmann

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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 $0.6$ mm. We present the first results that show an improvement in segmentation accuracy of imaging data acquired at a 9.4 Tesla MRI scanner.
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Thursday 7th July
Poster Session 2.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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