Preprocessing

This document describes the preprocessing steps performed before manually-guided tractography in the TractEM project. Code to perform the preprocessing may be found on github. Image registrations and transformations were performed using MATLAB and SPM 12. Diffusion reconstructions were performed using DSI Studio.

We began with a diffusion-weighted image set that had already been adjusted for head motion, eddy current effects, and distortion. For instance, the obtained HCP data sets were preprocessed as described by Glasser (2013).

A T1-weighted anatomical image from the same subject was coregistered to a b=0 image from the diffusion-weighted image set, if necessary.

A diffusion tensor reconstruction was performed on the diffusion-weighted image set to produce a fractional anisotropy image in the subject’s native space.

The DTI fractional isotropy image was registered to a Talairach space template image using an affine transformation. The template image was generated by applying the “pooled” affine transformation of Lancaster 2007 to the probabilistic ICBM152-space fractional anisotropy template image from the FSL atlas distribution (Mori 2005, Wakana 2007, Hua 2008).

The affine transformation was applied to the diffusion-weighted images, and the appropriate corresponding adjustment was made to the b vectors.

A Generalized Q-sampling Imaging (GQI) reconstruction (Yeh 2010), or a 4th-order spherical harmonic Q-ball reconstruction (QBI) (Tuch 2004, Descoteaux 2007), was applied to the transformed Talairach space diffusion-weighted images. GQI works best with a fairly dense sampling of directions and shells (e.g. Human Connectome Project), whereas QBI is applicable to less densely sampled single-shell data.

To provide the subject-specific lobar masks that are used in some of the tractography protocols, a multi-atlas segmentation algorithm was applied to the subject’s native space T1 image (Asman 2013,2014). This segmentation was transformed to Talairach space using the affine transformation determined above.

References

Asman AJ, Landman BA. Non-local statistical label fusion for multi-atlas segmentation. Medical image analysis. 2013;17(2):194-208. doi:10.1016/j.media.2012.10.002.

Asman AJ, Landman BA. Hierarchical Performance Estimation in the Statistical Label Fusion Framework. Medical image analysis. 2014;18(7):1070-1081. doi:10.1016/j.media.2014.06.005.

Descoteaux M, Angelino E, Fitzgibbons S, Deriche R. Regularized, fast, and robust analytical Q-ball imaging. Magn Reson Med. 2007 Sep;58(3):497 510. PubMed PMID: 17763358.

Glasser MF, Sotiropoulos SN, Wilson JA, et al. The Minimal Preprocessing Pipelines for the Human Connectome Project. NeuroImage. 2013;80:105 124. doi:10.1016/j.neuroimage.2013.04.127.

Lancaster JL, Tordesillas-Gutiérrez D, Martinez M, Salinas F, Evans A, Zilles K, Mazziotta JC, Fox PT. Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum Brain Mapp. 2007 Nov;28(11):1194-205. PubMed PMID: 17266101.

Tuch DS. Q-ball imaging. Magn Reson Med. 2004 Dec;52(6):1358-72. PubMed PMID: 15562495.

Yeh FC, Wedeen VJ, Tseng WY. Generalized q-sampling imaging. IEEE Trans Med Imaging. 2010 Sep;29(9):1626-35. doi: 10.1109/TMI.2010.2045126. Epub 2010 Mar 18. PubMed PMID: 20304721.

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