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Biomechanics-Informed Neural Networks for Myocardial Motion Tracking in MRI

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 12263))

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Abstract

Image registration is an ill-posed inverse problem which often requires regularisation on the solution space. In contrast to most of the current approaches which impose explicit regularisation terms such as smoothness, in this paper we propose a novel method that can implicitly learn biomechanics-informed regularisation. Such an approach can incorporate application-specific prior knowledge into deep learning based registration. Particularly, the proposed biomechanics-informed regularisation leverages a variational autoencoder (VAE) to learn a manifold for biomechanically plausible deformations and to implicitly capture their underlying properties via reconstructing biomechanical simulations. The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible. The proposed method is validated in the context of myocardial motion tracking on 2D stacks of cardiac MRI data from two different datasets. The results show that it can achieve better performance against other competing methods in terms of motion tracking accuracy and has the ability to learn biomechanical properties such as incompressibility and strains. The method has also been shown to have better generalisability to unseen domains compared with commonly used L2 regularisation schemes.

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Acknowledgement

This work was supported by EPSRC programme grant SmartHeart (EP/P001009/1). This research has been conducted mainly using the UK Biobank Resource under Application Number 40119. The authors wish to thank all UK Biobank participants and staff.

Author information

Authors and Affiliations

  1. Department of Computing, Imperial College London, London, UK

    Chen Qin, Chen Chen, Huaqi Qiu & Daniel Rueckert

  2. Institute for Digital Communications, School of Engineering, University of Edinburgh, Edinburgh, UK

    Chen Qin

  3. Data Science Institute, Imperial College London, London, UK

    Shuo Wang & Wenjia Bai

  4. Department of Brain Sciences, Imperial College London, London, UK

    Wenjia Bai

Authors
  1. Chen Qin
  2. Shuo Wang
  3. Chen Chen
  4. Huaqi Qiu
  5. Wenjia Bai
  6. Daniel Rueckert

Corresponding author

Correspondence toChen Qin.

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Anne L. Martel

  2. The University of British Columbia, Vancouver, BC, Canada

    Purang Abolmaesumi

  3. University College London, London, UK

    Danail Stoyanov

  4. École Centrale de Nantes, Nantes, France

    Diana Mateus

  5. EURECOM, Biot, France

    Maria A. Zuluaga

  6. Chinese Academy of Sciences, Beijing, China

    S. Kevin Zhou

  7. Sorbonne University, Paris, France

    Daniel Racoceanu

  8. The Hebrew University of Jerusalem, Jerusalem, Israel

    Leo Joskowicz

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Qin, C., Wang, S., Chen, C., Qiu, H., Bai, W., Rueckert, D. (2020). Biomechanics-Informed Neural Networks for Myocardial Motion Tracking in MRI. In: Martel, A.L.,et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_29

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