dHCP Fourth Data Release (April 2024)

Diffusion EDDY Pipeline

Inputs

From reconstruction pipeline: rawdata/sub-{subid}/ses-{sesid}

Outputs

The primary outputs of the diffusion EDDY preprocessing pipeline are:

Path: derivatives/dhcp_dmri_eddy_pipeline/sub-{subid}/ses-{sesid}

The complete list of outputs and QC reports generated by the diffusion EDDY pipeline are listed in the Diffusion EDDY pipeline section of the directory structure summary.

Pipeline

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 EDDY^3-6.

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 registration^8,9 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.

3. All QC metrics are available in the combined.tsv spreadsheet in the supplementary.

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