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Quantifying Residual Motion Artifacts in Fetal fMRI Data

  • Athena TaymourtashEmail author
  • Ernst Schwartz
  • Karl-Heinz Nenning
  • Daniel Sobotka
  • Mariana Diogo
  • Gregor Kasprian
  • Daniela Prayer
  • Georg Langs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11798)

Abstract

Fetal functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful tool for investigating brain development in utero, holding promise for generating developmental disease biomarkers and supporting prenatal diagnosis. However, to date its clinical applications have been limited by unpredictable fetal and maternal motion during image acquisition. Even after spatial realignment, these cause spurious signal fluctuations confounding measures of functional connectivity and biasing statistical inference of relationships between connectivity and individual differences. As there is no ground truth for the brain’s functional structure, especially before birth, quantifying the quality of motion correction is challenging. In this paper, we propose evaluating the efficacy of different regression based methods for removing motion artifacts after realignment by assessing the residual relationship of functional connectivity with estimated motion, and with the distance between areas. Results demonstrate the sensitivity of our evaluation’s criteria to reveal the relative strengths and weaknesses among different artifact removal methods, and underscore the need for greater care when dealing with fetal motion.

Keywords

Fetal fMRI Motion correction Functional connectivity 

Notes

Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765148.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Athena Taymourtash
    • 1
    Email author
  • Ernst Schwartz
    • 1
  • Karl-Heinz Nenning
    • 1
  • Daniel Sobotka
    • 1
  • Mariana Diogo
    • 2
  • Gregor Kasprian
    • 2
  • Daniela Prayer
    • 2
  • Georg Langs
    • 1
  1. 1.Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided TherapyMedical University of ViennaViennaAustria
  2. 2.Division for Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided TherapyMedical University of ViennaViennaAustria

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