TY - JOUR
T1 - Improved tractography using asymmetric fibre orientation distributions
AU - Bastiani, Matteo
AU - Cottaar, Michiel
AU - Dikranian, Krikor
AU - Ghosh, Aurobrata
AU - Zhang, Hui
AU - Alexander, Daniel C.
AU - Behrens, Timothy E.
AU - Jbabdi, Saad
AU - Sotiropoulos, Stamatios N.
N1 - Funding Information:
This work was supported by the UK EPSRC (grants EP/L023067/1 and EP/L022680/1). MB is supported by the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013/ ERC Grant Agreement no. 319456) and SJ is supported by the UK MRC (grant ref MR/L009013/1). We would also like to thank David Van Essen and Matt Glasser for fruitful discussions, Charles Chen for histological data preparation and acquisition and Paul McCarthy for providing the visualization software (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes). Data were provided [in part] by the Human Connectome Project (subject IDs: 100307, 100408, 101915, 102816, 103414, 103515, 103818, 105115, 105216, 106016), WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
Publisher Copyright:
© 2017 The Authors
PY - 2017/9
Y1 - 2017/9
N2 - Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and –x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity.
AB - Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and –x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity.
KW - Asymmetry
KW - Connectome
KW - Diffusion MRI
KW - Structural connectivity
KW - Tractography
UR - http://www.scopus.com/inward/record.url?scp=85022177804&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2017.06.050
DO - 10.1016/j.neuroimage.2017.06.050
M3 - Article
C2 - 28669902
AN - SCOPUS:85022177804
SN - 1053-8119
VL - 158
SP - 205
EP - 218
JO - NeuroImage
JF - NeuroImage
ER -