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Anatomy-Guided Convolutional Neural Network for Motion Correction in Fetal Brain MRI

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Abstract

Fetal Magnetic Resonance Imaging (MRI) is challenged by the fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion commonly occurs in between slices acquisitions. Motion correction for each slice is thus very important for reconstruction of 3D fetal brain MRI, but is highly operator-dependent and time-consuming. Approaches based on convolutional neural networks (CNNs) have achieved encouraging performance on prediction of 3D motion parameters of arbitrarily oriented 2D slices, which, however, does not capitalize on important brain structural information. To address this problem, we propose a new multi-task learning framework to jointly learn the transformation parameters and tissue segmentation map of each slice, for providing brain anatomical information to guide the mapping from 2D slices to 3D volumetric space in a coarse to fine manner. In the coarse stage, the first network learns the features shared for both regression and segmentation tasks. In the refinement stage, to fully utilize the anatomical information, distance maps constructed based on the coarse segmentation are introduced to the second network. Finally, incorporation of the signed distance maps to guide the regression and segmentation together improves the performance in both tasks. Experimental results indicate that the proposed method achieves superior performance in reducing the motion prediction error and obtaining satisfactory tissue segmentation results simultaneously, compared with state-of-the-art methods.

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Acknowledgements

This work was partially supported by NIH grants (MH117943).

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Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, Shanghai, China

    Yuchen Pei & Lisheng Wang

  2. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA

    Yuchen Pei, Fenqiang Zhao, Tao Zhong, Lufan Liao, Dinggang Shen & Gang Li

Authors
  1. Yuchen Pei

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  2. Lisheng Wang

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  3. Fenqiang Zhao

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  4. Tao Zhong

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  5. Lufan Liao

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  6. Dinggang Shen

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  7. Gang Li

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Corresponding author

Correspondence toGang Li.

Editor information

Editors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Mingxia Liu

  2. Rensselaer Polytechnic Institute, Troy, NY, USA

    Pingkun Yan

  3. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Chunfeng Lian

  4. United Imaging Intelligence, Shanghai, China

    Xiaohuan Cao

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Pei, Y.et al. (2020). Anatomy-Guided Convolutional Neural Network for Motion Correction in Fetal Brain MRI. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_39

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