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International Workshop on Machine Learning for Medical Image Reconstruction

MLMIR 2020: Machine Learning for Medical Image Reconstruction pp 48–59Cite as

Model-Based Learning for Quantitative Susceptibility Mapping

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12450))

Abstract

Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that estimates magnetic susceptibility of tissue from Larmor frequency offset measurements. The generation of QSM requires solving a challenging ill-posed field-to-source inversion problem. Inaccurate field-to-source inversion often causes large susceptibility estimation errors that appear as streaking artifacts in the QSM, especially in massive hemorrhagic regions. Recently, several deep learning (DL) QSM techniques have been proposed and demonstrated impressive performance. Due to the inherent non-existent ground-truth QSM references, these DL techniques used either calculation of susceptibility through multiple orientation sampling (COSMOS) maps or synthetic data for network training. Therefore, they were constrained by the availability and accuracy of COSMOS maps, or suffered from performance drop when the training and testing domains were different. To address these limitations, we present a model-based DL method, denoted as uQSM. Without accessing to QSM labels, uQSM is trained using the well-established physical model. When evaluating on multi-orientation QSM datasets, uQSM achieves higher levels of quantitative accuracy compared to TKD, TV-FANSI, MEDI, and DIP approaches. When qualitatively evaluated on single-orientation datasets, uQSM outperforms other methods and reconstructed high quality QSM.

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Acknowledgement

We thank Professor Jongho Lee for sharing the multi-orientation QSM datasets.

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Authors and Affiliations

  1. Center for Imaging Research, Medical College of Wisconsin, Milwaukee, WI, USA

    Juan Liu & Kevin M. Koch

  2. Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA

    Juan Liu & Kevin M. Koch

  3. Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA

    Kevin M. Koch

Authors
  1. Juan Liu
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Correspondence to Juan Liu .

Editor information

Editors and Affiliations

  1. University of British Columbia, Vancouver, BC, Canada

    Farah Deeba

  2. New York University, New York City, NY, USA

    Patricia Johnson

  3. Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany

    Tobias Würfl

  4. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jong Chul Ye

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Liu, J., Koch, K.M. (2020). Model-Based Learning for Quantitative Susceptibility Mapping. In: Deeba, F., Johnson, P., Würfl, T., Ye, J.C. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2020. Lecture Notes in Computer Science(), vol 12450. Springer, Cham. https://doi.org/10.1007/978-3-030-61598-7_5

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  • DOI: https://doi.org/10.1007/978-3-030-61598-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61597-0

  • Online ISBN: 978-3-030-61598-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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