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The diffusion pipeline I

For a complete and detailed description of all the steps involved in the dHCP neonatal diffusion MRI (dMRI) data processing pipeline, the reader is referred elsewhere1. The main processing steps are briefly summarised below:

  1. For each phase encoding (PE) direction, the diffusion un-weighted b0 volume pairs least-affected by intra-volume motion are automatically selected. The dataset is then re-organised by moving the least-affected b0 volume and the volumes that follow (until the end of the acquisition) at the beginning of the 4D raw data file.

  2. Field maps for correcting susceptibility-induced distortions are estimated using FSL TOPUP2.

  3. Distortions caused by susceptibility, between-volume motion, within-volum motion, motion-induced signal drop-out, motion-by-susceptibility interactions, and eddy currents are corrected; outlier slices are detected and replaced in raw distorted space. All these steps use FSL EDDY3-6.

  4. A super-resolution algorithm7 is applied along the slice-selection direction, to achieve isotropic resolution of 1.5 mm.

  5. Post-processing using traditional tensor fitting, as well as FSL's BedpostX for multishell data13 is applied.

  6. Diffusion data are aligned to high-resolution structural (T2-weighted) space using boundary-based registration8,9 on the average attenuation volume for the b=1000 s/mm2 shell (i.e. b1k/b0). This transformation is combined with a non-linear registration10 of the T2w volume to the 40 weeks template11 to allow transformations between diffusion and atlas spaces.

Diffusion MRI QC

  1. Numerous quality assurance metrics are calculated by the EDDY QC tools12. Four of these are specifically compared against the population distribution to flag outliers for manual inspection and potential exclusion:

    1. Mean signal-to-noise ratio (SNR) from the b0 volumes

    2. Mean contrast-to-noise ratio (CNR) for each b-shell, i.e., 400, 1000 and 2600 s/mm2.

  2. All QC metrics are then converted to Z-scores and averaged, to generate a summary QC metric.

References

  1. Bastiani, M., Andersson, J.L.R., Cordero-Grande, L., Murgasova, M., Hutter, J., Price, A.N., Makropoulos, A., Fitzgibbon, S.P., Hughes, E., Rueckert, D., Victor, S., Rutherford, M., Edwards, A.D., Smith, S.M., Tournier, J.D., Hajnal, J.V., Jbabdi, S., and Sotiropoulos, S.N. Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project Neuroimage (2019), 185: 750-763. DOI: 10.1016/j.neuroimage.2018.05.064

  2. Andersson, J.L., Skare, S., and Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging Neuroimage (2003), 20(2): 870-888. DOI: 10.1016/S1053-8119(03)00336-7

  3. Andersson, J.L., and Sotiropoulos, S.N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging Neuroimage (2016), 125: 1063-1078. DOI: 10.1016/j.neuroimage.2015.10.019

  4. Andersson, J.L.R., Graham, M.S., Drobnjak, I., Zhang, H., and Campbell, J. Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data NeuroImage (2018), 171: 277-295. DOI: 10.1016/j.neuroimage.2017.12.040

  5. Andersson, J.L.R., Graham, M.S., Drobnjak, I., Zhang, H., Filippini, N., and Bastiani, M. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement Neuroimage (2017), 152: 450-466. DOI: 10.1016/j.neuroimage.2017.02.085

  6. Andersson, J.L.R., Graham, M.S., Zsoldos, E., and Sotiropoulos, S.N. Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images Neuroimage (2016), 141: 556-572. DOI: 10.1016/j.neuroimage.2016.06.058

  7. Kuklisova-Murgasova, M., Quaghebeur, G., Rutherford, M.A., Hajnal, J.V., and Schnabel, J.A. Reconstruction of fetal brain MRI with intensity matching and complete outlier removal Med Image Anal (2012), 16(8): 1550-1564. DOI: 10.1016/j.media.2012.07.004

  8. Greve, D.N., and Fischl, B. Accurate and robust brain image alignment using boundary-based registration Neuroimage (2009), 48(1): 63-72. DOI: 10.1016/j.neuroimage.2009.06.060

  9. Jenkinson, M., Bannister, P., Brady, M., and Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain image Neuroimage (2002), 17(2): 825-841. DOI: 10.1006/nimg.2002.1132

  10. Andersson, J.L.R., Jenkinson, M., and Smith, S. Non-linear registration, aka spatial normalisation FMRIB technical report TR07JA2 (2010).

  11. Schuh, A., Makropoulos, A., Robinson, E.C., Cordero-Grande, L., Hughes, E., Hutter, J., Price, A.N., Murgasova, M., Teixeira, R.P.A.G., Tusor, N., Steinweg, J.K., Victor, S., Rutherford, M.A., Hajnal, J.V., Edwards, A.D., and Rueckert, D. Unbiased construction of a temporally consistent morphological atlas of neonatal brain development bioRxiv (2018), 251512. DOI: 10.1101/251512

  12. Bastiani, M., Cottaar, M., Fitzgibbon, S.P., Suri, S., Alfaro-Almagro, F., Sotiropoulos, S.N., Jbabdi, S., and Andersson, J.L.R. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction Neuroimage (2019), 184: 801-812. DOI: 10.1016/j.neuroimage.2018.09.073

  13. Jbabdi S, Sotiropoulos S.N., Savio A., Grana M., Behrens T.E.J.. Model-based analysis of multishell diffusion MR data for tractography: How to get over fitting problems. Magn Reson Med (2012), DOI:10.1002/mrm.24204