How to correct for distortions in spinal cord diffusion MRI data?

Context: distortions in EPI

Echo planar imaging (EPI) data usually suffer from susceptibility artifacts, manifesting as pixel displacement in the phase-encoding direction. For gradient echo EPI (used in fMRI), intravoxel dephasing also causes a decrease of the magnitude signal (Note: the information is lost and cannot be recovered with processing techniques). In diffusion MRI, the strong and rapidly-switching gradients also induce Eddy currents, which manifest as translation, scaling and shearing. Note that scaling and shearing displacements increase as a function of the distance from the center of the FOV [Bodammer et al. 2004], which is useful information for what comes later in this post.

What are the traditional methods for the brain?

Popular methods include:

  • Mapping the static B0 field using a fieldmapping sequence and then estimating and correcting pixel displacement [Jezzard et al. 1995].
  • Acquiring EPI data with two opposing polarities of the phase-encode gradients (technique also known as “blip up / blip down”). Both data are submitted to distortions with opposite displacement direction. These two data are then used to estimate a distortion field that brings a distorted EPI to the “mid-space”, effectively correcting susceptibility-related distortions [Andersson et al. 2003]. Such method is notably implemented in FSL’s topup.

Why it doesn’t work (as well) in the spinal cord

It is difficult to acquire a robust and accurate B0 fieldmap in the spinal cord region, partly due to the presence of strong CSF flow, low SNR and dynamic B0 variations in the vicinity of the lungs – up to 70Hz B0 offset were reported at 3T [Verma et al. 2014]. Moreover, the presence of multiple 2𝛑 wraps makes it difficult for unwrapping algorithms to recover an unwrap field map without biases. The danger of using an incorrect fieldmap is that you might end up adding more distortions than already present in the image.

Regarding the blip/up blip/down technique for correcting susceptibility distortions, while this technique usually works well for the brain because in general the entire volume is included in the field of view, in the case of the spinal cord where reduced field of view techniques are used [Samson et al. 2016] (precisely to decrease the amount of distortions at the first place), the two images with opposite gradient polarities end up having different information content, which could then lead to a wrong estimation of the deformation field (the one that finds the “mid-space” between the two images). Beyond the problem related to the reduced field-of-view, a mis-match of information content can also happen because of flow artifacts (producing a strong signal decrease in the hyperintense CSF on T2-like images) and tissue motion (producing signal loss on DW images). When it comes to Eddy-current distortions, the problem is not as bad in the cord vs. in the brain, because the spinal cord is a thin structure (~1 cm diameter) and is usually close to the isocenter (i.e. within the gradient linearity area) and is X-Y centered in the FOV. As mentioned earlier, the scaling/shearing displacements increase with X and Y, so we typically see minimum impact of those affine distortion parameters within the spinal cord region. Eddy-current-related translations could be present however, and those can be easily and robustly corrected using regularized slice-wise alignment [Cohen-Adad et al. 2015]. So, like for the fieldmap approach, a proper estimation of the deformation field is difficult and could lead to spurious deformation.

What are the alternatives?

An alternative method to deal with distortions is based on the non-linear registration of an EPI over a non-distorted reference image [Ardekani et al. 2005]. The reference image is typically a T2-weighted fast spine-echo sequence, that covers the same region as the DW-EPI, and with a similar (or better) spatial resolution. The registration method could be purely image-based (e.g. calculating the mutual information or cross-correlation between the two images), or could use a cord segmentation as pre-registration steps to make the registration more robust & accurate [De Leener et al. 2017]. This method is advantageous because no additional volume acquisition is required, except a reference image without distortion – which is incidentally generally acquired by default in standard protocols.

Of course, it is always a good idea to acquire data with minimal distortions in the first place (by minimizing the echo spacing, optimizing shimming, using reduced FOV techniques, etc.).

OK I get the theory. Now, what can I do with my data?

An example of processing pipeline that involves slice-based correction of DW time series and registration of the mean motion-corrected DW data to a template is available here. It has been tested in an open-access database of ~240 subjects [Cohen-Adad et al. 2019].

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