Preprocessing

By preprocessing, we are referring to converting your images from the raw DICOM to FA images for each subject and quality controlling along the way to remove scans with abnormalities and artifacts.There can be several ways to pre-process your diffusion weighted data in order to maximize the quality and efficiency of your processing. We will therefore not require a specific protocol to be followed with any particular software, as long as the appropriate steps are performed. This will allow maximal integration with current pipelines and ensure optimal processing per site if available, and allow sites to:

1. Process data efficiently with respect to acquisition parameters (eg., do not spend time on HARDI-specific algorithms if you only have 6/12 directions collected)
2. Take advantage of your scanning protocols
   • if you know you can calculate FA more robustly using one of many alternate methods, go for it!
   • maximize the quality for your scans (denoising/ removing artifacts etc.)
3. Keep things in line with current/future projects, and non ENIGMA-related investigations you are working on.

If you have FA measures maps calculated and registered already, we can work with you to include them into the Pipeline rather than to re-run everything from the start. Therefore, if you have discovered workflows and methods that fit your data well to best improve SNR, this would be ideal.

If you have already processed your data, please email support.enigmaDTI@ini.usc.edu to let us know your processing workflow. Also if you would like to update this page with any particulars for your methods, please let us know and we would be happy to work in additional options. For those that have yet to process DTI data, various suggestions are outlined here. A basic series of steps are as follows: NOTE: most of this can be done in multiple ways depending on your data. please do not hesitate to contact us for support .

Convert DICOM images to DWI set and T1-weighted set and other data acquired.

• Determine how your DWI set(s) are organized
• How many many acquisitions do you have? Multiple acquisitions can be merged for optimal signal-to-noise ratio.
• How many b0s do you have and where are they with respect to the full series? (Often the b0 image(s) is/are the first volumes in the DWI set)
• If you have multiple b0, were they acquired with the same encoding gradient? If not, slight variations in processing will be needed.

Denoising

There are several different denoising methods that can be appropriately used for your data. A few of them are listed below. NOTE: This is the first step that needs to be taken after dicom to nifti conversion. Before deciding on which method, you will need to check

• Whether or not the data acquired was zero-filled at acquisition (typically done on GE scanners). If it is, LPCA will not work effectively and consideration of AONLM/MP-PCA filters may be a better choice

A few of the different denoising methods include:

• LPCA
  • "takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging and reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach [Manjón et al., 2013]."
    • download located here and filter documentation located here
• AONLM
  • "designed for spatially varying noise typically presents in parallel imaging, information regarding the local image noise level is used to adjust the amount of denoising strength of the filter [Manjón et al., 2011]."
    • download located here and filter documentation located here
• MP-PCA
  • "exploits the intrinsic redundancy in diffusion MRI using universal properties of the eigenspectrum of random covariance matrices, and removes noise-only principal components -- thereby enabling signal-to-noise ratio enhancements [Veraart et al., 2016]."
    • code located here and an MRtrix wrapper is also available called dwidenoise

example of raw dwi gradient direction (left) and LPCA denoised dwi gradient direction (right):

Gibbs ringing artifact correction

Gibbs-ringing is an artifact that is often displayed in MRI images as spurious oscillations nearby sharp image gradients at tissue boundaries. This can be corrected using the method of local subvoxel-shifts proposed by Kellner et al., 2015.

• You can correct your data using the original code -- unring, or MRtrix's wrapper implementation mrdegibbs

Notes:

• Should be performed directly after denoising and before any other preprocessing steps
• This method was developed to work on images acquired with full k-space coverage
  • "...partial Fourier acquisition demonstrates that incomplete k‐space acquisition schemes propagate the artifact in an obscure nonobvious manner, which might lead to a misinterpretation of image features"
  • Therefore it is imperative to check your data. You can find this information regarding partial k-space coverage in the DICOM tuple (0018,0022)

Correct for Eddy Current distortions and movement

• A convenient option for this is FSL’s eddy command.

  • There are a few parameters you will need to learn about your data before performing this step. They include:
• Additionally, MRtrix has a wrapper for this called dwipreproc

Create a mask for your data.

• FSL’s bet2 offers a solution that is quite robust for many datasets.
• Additionally, MRtrix's dwi2mask command utilizes directional information to generate a mask that may offer a better option for your data
• HD-BET?

Bias field correction

Often times, data is affected by B1 field inhomogeneity resulting in signal intensity differences throughout the image. A DWI series can be corrected for this using:

• ANTs or FSL FAST
• There is also a DWI wrapper for these two options using MRtrix called dwibiascorrect

Note: (explain how an iterative N4/mask process might be helpful here)

Note: if you only have one phase encoding direction, an additional EPI-Correction script is available in the 2. ENIGMA-DTI Epi-Correction repository