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Super Resolution Convolutional Neural Networks for Increasing Spatial Resolution of \(^{1}\)H Magnetic Resonance Spectroscopic Imaging

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Medical Image Understanding and Analysis (MIUA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 723))

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Abstract

Proton magnetic resonance spectroscopic imaging (\(^{1}\)H-MRSI) provides noninvasive information regarding metabolic activity within the tissues. One of the main problems of \(^{1}\)H-MRSI is low spatial resolution due to clinical scan time limitations. Advanced post-processsing algorithms, like convolutional neural networks (CNN) might help with generation of super resolution \(^{1}\)H-MRSI. In this study, the application of super resolution convolutional neural networks (SRCNN) for increasing the spatial resolution of \(^{1}\)H-MRSI is presented. Fluid Attenuated Inversion Recovery (FLAIR), T1-weighted, T2-weighted magnetic resonance imaging (MRI) data and a fused MRI, which contained the three different structural MR images in each RGB channel, were used in training the SRCNN scheme. The spatial resolution of \(^{1}\)H-MRSI images were increased by a factor of three using the models trained with the anatomical MR images. The results of the proposed technique were compared with bicubic resampling in terms of peak signal to noise ratio and root mean square error. Our results indicated that SRCNN would contribute to reconstructing higher resolution \(^{1}\)H-MRSI.

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Notes

  1. 1.

    https://sourceforge.net/projects/bric1936/files/MATLAB/.

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Acknowledgements

This study was supported by the Royal Society through the Newton Mobility Grant NI150340. MCVH is funded by Row Fogo Charitable Trust, grant no. BRO-D.FID3668413.

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Authors and Affiliations

  1. Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey

    Sevim Cengiz & Esin Ozturk-Isik

  2. Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK

    Maria del C. Valdes-Hernandez

Authors
  1. Sevim Cengiz
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  2. Maria del C. Valdes-Hernandez
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  3. Esin Ozturk-Isik
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Correspondence to Esin Ozturk-Isik .

Editor information

Editors and Affiliations

  1. Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom

    María Valdés Hernández

  2. University of León, León, Spain

    Víctor González-Castro

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Cengiz, S., Valdes-Hernandez, M.d.C., Ozturk-Isik, E. (2017). Super Resolution Convolutional Neural Networks for Increasing Spatial Resolution of \(^{1}\)H Magnetic Resonance Spectroscopic Imaging. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_56

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  • DOI: https://doi.org/10.1007/978-3-319-60964-5_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60963-8

  • Online ISBN: 978-3-319-60964-5

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