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Image Quality Assessment for Population Cardiac Magnetic Resonance Imaging

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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. MRI is arguably the most comprehensive imaging modality for noninvasive and nonionizing imaging of the heart and great vessels and, hence, most suited for population imaging cohorts. Ensuring full coverage of the left ventricle (LV)  is a basic criterion of CMR image quality. Complete LV coverage, from base to apex, precedes accurate cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in large imaging cohorts. In this chapter, we propose a novel automatic method to check the coverage of LV from CMR images by using Fisher discriminative and dataset invariance (FDDI) three-dimensional (3D) convolutional neural networks (CNN)  independently of image-acquisition parameters, such as imaging device, magnetic field strength, variations in protocol execution, etc. The proposed model is trained on multiple cohorts of different provenance to learn the appearance and identify missing basal and apical slices. To address this, a two-stage framework is proposed. First, the FDDI 3D CNN extracts high-level features in the common representation from different CMR datasets using adversarial approach; then these image features are used to detect missing basal and apical slices. Compared with the traditional 3D CNN strategy, the proposed FDDI 3D CNN can minimize the within-class scatter and maximize the between-class scatter, which can be adapted to other CMR image data for LV coverage assessment.

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Notes

  1. 1.

    http://www.cardiacatlas.org/studies/determine/.

  2. 2.

    http://www.cardiacatlas.org/studies/mesa/.

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Author information

Authors and Affiliations

  1. Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Sheffield, Sheffield, UK

    Le Zhang

  2. Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK

    Marco Pereañez & Alejandro F. Frangi

  3. Division of Cardiovascular Medicine, Oxford Center for Clinical Magnetic Resonance Research (OCMR), University of Oxford, John Radcliffe Hospital, Oxford, UK

    Stefan K. Piechnik & Stefan Neubauer

  4. Cardiovascular Medicine at the William Harvey Research Institute, Queen Mary University of London and Barts Heart Center, Barts Health NHS Trust, London, UK

    Steffen E. Petersen

Authors
  1. Le Zhang
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  2. Marco Pereañez
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  3. Stefan K. Piechnik
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  4. Stefan Neubauer
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  5. Steffen E. Petersen
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  6. Alejandro F. Frangi
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Corresponding author

Correspondence to Le Zhang .

Editor information

Editors and Affiliations

  1. Bethesda Research Lab, PAII Inc., Bethesda, MD, USA

    Le Lu

  2. Nvidia Corporation, Bethesda, MD, USA

    Xiaosong Wang

  3. School of Computer Science, University of Adelaide, Adelaide, SA, Australia

    Gustavo Carneiro

  4. Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA

    Lin Yang

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Zhang, L., Pereañez, M., Piechnik, S.K., Neubauer, S., Petersen, S.E., Frangi, A.F. (2019). Image Quality Assessment for Population Cardiac Magnetic Resonance Imaging. In: Lu, L., Wang, X., Carneiro, G., Yang, L. (eds) Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-13969-8_15

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  • DOI: https://doi.org/10.1007/978-3-030-13969-8_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13968-1

  • Online ISBN: 978-3-030-13969-8

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