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.
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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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Sobotka, D. et al. (2019). Reproducibility of Functional Connectivity Estimates in Motion Corrected Fetal fMRI. In: Wang, Q., et al. Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis. PIPPI SUSI 2019 2019. Lecture Notes in Computer Science(), vol 11798. Springer, Cham. https://doi.org/10.1007/978-3-030-32875-7_14
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DOI: https://doi.org/10.1007/978-3-030-32875-7_14
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