[12]{.chapter-number}  [Orientation Distribution Function Reconstruction]{.chapter-title}

12 Orientation Distribution Function Reconstruction

One of the primary aims of collecting diffusion MRI data is to estimate the voxelwise Orientation Distribution Fuctions. These functions describe how water diffuses within each voxel. There are many different methods for reconstructing such functions. The A2CPS project relies on two methods, one that produces regional summary metrics, and another for tractography. This kit describes the former.

12.1 Starting Project

12.1.1 Locate Data

On TACC, the neuroimaging data are stored underneath the releases. For example, data release v2.#.# is underneath

pre-surgery/mris

These outputs are under derivatives/qsirecon_fsl_dtifit. There are three groups of outputs.

#| eval: false
$ ls qsirecon_fsl_dtifit
dtifit  qsirecon-fsl  split_shells

These outputs were generated in sequence. First, the qsirecon-fsl were generated by the qsirecon workflow reorient_fslstd, which takes the outputs of Chapter 11 and reorients them into the format that is expected by the FSL Diffusion Toolbox. For details on the organization of that folder, see the QSIRecon documentation. Then, the split_shells outputs were generated. They contain the reoriented outputs, but split into different bvals. For example

$ tree -l qsirecon_fsl_dtifit/split_shells/sub-10010
qsirecon_fsl_dtifit/split_shells/sub-10010
└── ses-V1
    └── dwi
        ├── sub-10010_ses-V1_acq-b0_space-T1w_dwi.bval 
        ├── sub-10010_ses-V1_acq-b0_space-T1w_dwi.bvec 
        ├── sub-10010_ses-V1_acq-b0_space-T1w_dwi.nii.gz 
        ├── sub-10010_ses-V1_acq-b1000_space-T1w_dwi.bval 
        ├── sub-10010_ses-V1_acq-b1000_space-T1w_dwi.bvec 
        ├── sub-10010_ses-V1_acq-b1000_space-T1w_dwi.nii.gz 
        ├── sub-10010_ses-V1_acq-b2000_space-T1w_dwi.bval 
        ├── sub-10010_ses-V1_acq-b2000_space-T1w_dwi.bvec 
        ├── sub-10010_ses-V1_acq-b2000_space-T1w_dwi.nii.gz 
        ├── sub-10010_ses-V1_acq-b3000_space-T1w_dwi.bval 
        ├── sub-10010_ses-V1_acq-b3000_space-T1w_dwi.bvec 
        ├── sub-10010_ses-V1_acq-b3000_space-T1w_dwi.nii.gz 
        ├── sub-10010_ses-V1_acq-b500_space-T1w_dwi.bval 
        ├── sub-10010_ses-V1_acq-b500_space-T1w_dwi.bvec 
        └── sub-10010_ses-V1_acq-b500_space-T1w_dwi.nii.gz 

2 directories, 15 files

Finally, the dtifit outputs were generated by running FSL’s dtifit on different combinations of the bvalues.

$ ls qsirecon_fsl_dtifit/dtifit/
b1000  b2000  b3000  multishell

Most of those are self-explanatory (e.g., the b1000 subfolder contains the fits with the b1000 shell). The multishell outputs were generated from the full dMRI scan (that is, all shells). Which of these outputs to use is up to the individual researcher. According to the ABCD, the protocol and analysis plan on which A2CPS dMRI is based, many researchers may prefer the multishell outputs. That said, the tensor model is known to fit poorly to diffusion data with bvalues larger than about 1500 Jensen et al. (2005), which is a majority of this sequence. Given that, some researchers may prefer the b1000 outputs, but note that these are based on only 15 directions.

The contents of each of the dtifit subfolders are in a “bidsish” organization, containing the standard outputs of dtifit, grouped by subject and session.

# within qsirecon_fsl_dtifit/dtifit/b1000
$ tree sub-10220
sub-10220
└── ses-V1
    └── dwi
        ├── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_FA.nii.gz
        ├── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_L1.nii.gz
        ├── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_L2.nii.gz
        ├── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_L3.nii.gz
        ├── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_MD.nii.gz
        ├── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_MO.nii.gz
        ├── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_S0.nii.gz
        ├── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_sse.nii.gz
        ├── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_tensor.nii.gz
        ├── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_V1.nii.gz
        ├── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_V2.nii.gz
        └── sub-10220_ses-V1_acq-b1000_space-T1w_dwi_V3.nii.gz

2 directories, 12 files

12.1.2 Extract Data

Most of these outputs are nifti images, and so any tool capable of reading them will work. Some neuroimaging graphical user interfaces also have built in tools for viewing the tensors. For example, by default the FSL viewer fsleyes will accept as input a folder with dtifit outputs and by default display the estimated tensors.

$ fsleyes dwi

There are several options for refining the display. For example, fsleyes can also produce variations on color FA maps.

12.2 Considerations While Working on the Project

12.2.1 Data Generation

These outputs were generated by the qsirecon-fsl_dtifit_app. As discussed above, the orientation distribution functions were reconstructed with the standard diffusion tensor imaging model, as estimated by FSL’s dtifit (using the weighted-least squares option).

For a good general introduction to DTI, see the QSIRecon documentation.

Note that the tensor model is known to fit poorly to diffusion data with bvalues larger than about 1500 Jensen et al. (2005). Several advanced models have been proposed, and the A2CPS neuroimaging DIRC is actively testing these. Outputs from another model are expected to be available in the release 4 series.

Jensen, J. H., Helpern, J. A., Ramani, A., Lu, H., & Kaczynski, K. (2005). Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 53(6), 1432–1440. https://doi.org/10.1002/mrm.20508

12.2.2 Variability Across Scanners

Many MRI biomarkers exhibit variability across the scanners, which may confound some analyses. For an up-to-date assessment of the issue and overview of current thinking, please see Confluence.

12.2.3 Data Quality

As with any MRI derivative, all pipeline derivatives have been included. This means that products were included regardless of their quality, and so some products may have been generated from images that are known to have poor quality—rated “red”, or incomparable. For details on the ratings and how to exclude them, see Appendix A. Additionally, extensive QC has not yet been performed on the derivatives themselves, and so there may be cases where pipelines produced atypical outputs. For an overview of planned checks, see Confluence.

12.2.4 Citations

The standard DTI reference is Basser et al. (1994). These outputs used a workflow from QSIRecon,

Basser, P. J., Mattiello, J., & LeBihan, D. (1994). MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 66(1), 259–267. https://doi.org/10.1016/S0006-3495(94)80775-1

In publications or presentations including data from A2CPS, please include the following statement as attribution:

Data were provided [in part] by the A2CPS Consortium funded by the National Institutes of Health (NIH) Common Fund, which is managed by the Office of the Director (OD)/ Office of Strategic Coordination (OSC). Consortium components and their associated funding sources include Clinical Coordinating Center (U24NS112873), Data Integration and Resource Center (U54DA049110), Omics Data Generation Centers (U54DA049116, U54DA049115, U54DA049113), Multi-site Clinical Center 1 (MCC1) (UM1NS112874), and Multi-site Clinical Center 2 (MCC2) (UM1NS118922).

Note

The following published papers should be cited when referring to A2CPS Protocol and Biomarkers: Sluka et al. (2023) Berardi et al. (2022)

Berardi, G., Frey-Law, L., Sluka, K. A., Bayman, E. O., Coffey, C. S., Ecklund, D., Vance, C. G. T., Dailey, D. L., Burns, J., Buvanendran, A., McCarthy, R. J., Jacobs, J., Zhou, X. J., Wixson, R., Balach, T., Brummett, C. M., Clauw, D., Colquhoun, D., Harte, S. E., … Wandner, L. D. (2022). Multi-site observational study to assess biomarkers for susceptibility or resilience to chronic pain: The acute to chronic pain signatures (A2CPS) study protocol. Frontiers in Medicine, 9. https://doi.org/10.3389/fmed.2022.849214

Sluka, K. A., Wager, T. D., Sutherland, S. P., Labosky, P. A., Balach, T., Bayman, E. O., Berardi, G., Brummett, C. M., Burns, J., Buvanendran, A., et al. (2023). Predicting chronic postsurgical pain: Current evidence and a novel program to develop predictive biomarker signatures. Pain, 164(9), 1912–1926. https://doi.org/10.1097/j.pain.0000000000002938

Sadil, P., Arfanakis, K., Bhuiyan, E. H., Caffo, B., Calhoun, V. D., Clauw, D. J., DeLano, M. C., Ford, J. C., Gattu, R., Guo, X., Harris, R. E., Ichesco, E., Johnson, M. A., Jung, H., Kahn, A. B., Kaplan, C. M., Leloudas, N., Lindquist, M. A., Luo, Q., … Chronic Pain Signatures Consortium, T. A. to. (2024). Image processing in the acute to chronic pain signatures (A2CPS) project. bioRxiv. https://doi.org/10.1101/2024.12.19.627509

When using neuroimaging derivatives, please also cite Sadil et al. (2024).