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Reproducibility of Functional Connectivity Estimates in Motion Corrected Fetal fMRI

  • Daniel SobotkaEmail author
  • Roxane Licandro
  • Michael Ebner
  • Ernst Schwartz
  • Tom Vercauteren
  • Sebastien Ourselin
  • Gregor Kasprian
  • Daniela Prayer
  • Georg Langs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11798)

Abstract

Preprocessing and motion correction are essential steps in resting state functional Magnetic Resonance Imaging (rs-fMRI) of the fetal brain. They aim to address the difficult task of removing artefacts caused by fetal movement or maternal breathing, and aim to suppress erroneous signal correlations caused by motion. While preprocessing standards have been established in the adult brain, motion correction of fetal rs-fMRI and subsequent interpretation of processed data is still challenging. Here, we evaluate the effect of different preprocessing methods and motion correction on rs-fMRI sequences by assessing reproducibility of functional connectivity estimates. For slice-based motion correction of 4D fetal rs-fMRI, we extend a high-resolution reconstruction approach presented for structural fetal MRI. Correlation, standard deviation and structural similarity index are evaluated on the whole cortex, on specific regions and at different gestational ages. Results show improved reproducibility and signal interpretability after preprocessing with motion correction enabling the quantification of long-range correlation patterns of the developing default mode network in the fetal brain.

Notes

Acknowledgement

This work was supported by The Wellcome Trust [WT101957; 203148/Z/16/Z], the Engineering and Physical Sciences Research Council [NS/A000027/1; NS/A000049/1], Austrian Science Fund FWF (I2714-B31) and EU H2020 Marie Sklodowska-Curie No 765148.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Daniel Sobotka
    • 1
    Email author
  • Roxane Licandro
    • 1
    • 3
  • Michael Ebner
    • 4
  • Ernst Schwartz
    • 1
  • Tom Vercauteren
    • 4
  • Sebastien Ourselin
    • 4
  • 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 of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided TherapyMedical University of ViennaViennaAustria
  3. 3.Computer Vision Lab, Institute of Visual Computing and Human-Centered TechnologyTU WienViennaAustria
  4. 4.School of Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK

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