The analysis of diffusion weighted MRI (DWI) data has become increasingly important in neuroimaging. DWI images contain information about the anatomy essential for quantifying white matter architecture and connectivity patterns. DWI images reveal details about the connectional and microstructural anatomy of the living human brain that are inaccessible to any other in vivo imaging modality. Diffusion tensor images (DTI), in particular, describe the local diffusion process or the 3D probability profile of water diffusion in tissue using a 3×3 symmetric positive definite matrix at each voxel.
The analysis of white matter integrity in brain development, aging, and neurological conditions is a relatively new and active area of neuroscientific and clinical research [[20
]]. One approach to quantifying white matter anatomy with DWI data is to compute scalar summaries of the diffusion weighted images such as fractional anisotropy (FA), trace, and apparent diffusion coefficient (ADC). A more complex alternative is to examine reconstructed fiber tractography, which reveals systems-level information not directly contained in the voxel measures [[6
Tract-based analysis of populations allows one to quantify the effects of natural and disease processes using a rich set of information, including diffusion values along the tracts, integrity of the fiber bundles, as well as shape and deformation quantification of tract geometry [[7
]]. One of the most frequently cited group analysis streams in neuroimaging is tract-based spatial statistics (TBSS) [[32
]] available as part of the FSL software suite from the FMRIB group at Oxford. This analysis pipeline projects the FA data from all subjects onto a mean FA tract skeleton, before applying voxel-wise cross-subject statistics.
For group studies and for labeling the anatomy of an individual subject it is frequently important to establish inter-subject correspondence, either directly or through a common coordinate system. Since structural MRI images such as T1- and T2-weighted acquisitions contain little information about the interior of the cerebral white matter, it is reasonable to base cross-subject alignment in these regions on properties of the DWI data, either directly or indirectly. As an example of the former, several algorithms have been introduced that make use of information directly derived from the DTI images. One way to define such a framework is to extract scalar-valued summaries from the DTI tensors at each voxel (trace, fractional anisotropy,…) and define a similarity metric on these quantities. Guimond et al. suggested a modified version of the demon’s algorithm for aligning T2 and DTI datasets by using transformation-invariant tensor characteristics (eigenvalues) extracted from the diffusion tensors [[16
]]. Alexander et al. build upon a more complete set of diffusion information and their alignment of structural and DTI scans is proposed via an elastic matching algorithm using the tensor difference as a similarity metric [[1
]]. Ruiz-Alzola et al. proposed a template matching algorithm, where a similarity function derived from the generalized correlation matrix is computed directly from the diffusion tensors [[30
]]. The inclusion of information beyond the scalar metrics has also been shown to be advantageous in some studies. For example Park et al. found that considering all six components of the diffusion tensors resulted in improved alignment accuracy [[26
]]. In the TBSS framework, the spatial alignment of the input data is based on computing an affine and nonlinear warp combination to align the fractional anisotropy volume of each subject with an atlas coordinate system. Another approach is to use the reconstructed fiber tracts themselves as a basis of spatial normalization. Objective functions can be constructed that ensure that the size, shape and spatial location of white matter fiber tracts from different individuals are in corresponding locations [[36
]]. Even though in the case of these approaches correspondence between subjects is established based upon (some portion of) the diffusion data and thus more information about the white matter is considered, the lower spatial resolution, contrast-to-noise ratio (CNR) and geometric distortions of diffusion images often limit the accuracy and robustness of the outcomes.
In indirect registration approaches, structural acquisitions such as T1- and T2-weighted scans of corresponding subjects are used to compute image correspondence. As DWI acquisitions are typically acquired in the same session as the structural images, the alignment of structural and DTI images of the same subject usually involves just rigid or linear motion correction (as long as a separate EPI distortion correction precedes this registration in the diffusion image preprocessing pipeline). The composition of these registrations then allows one to transform the DWI data into a common coordinate system. As mentioned above, the advantage of these methods is inherently related to the significantly higher spatial resolution, contrast-to-noise ratio and far less geometric distortion of the structural acquisitions. However, these images contain little information about the architecture of the interior of the white matter, and thus the alignment in these regions might be somewhat arbitrary when based only on structural scans. Significant research effort has been devoted to incorporating diffusion derived measurements into nonlinear cross-subject alignment with the goal of improving the alignment of these areas [[26
]]. Recently, various similarity metrics (quantifying the level of similarity of diffusion features at corresponding spatial locations) have been suggested for use in nonlinear cross-subject DWI alignment [[2
In this paper we evaluate the performance of our combined volume and surface (CVS) algorithm [[28
]] for the purposes of fiber bundle alignment and show that high accuracy cross-subject registration based on structural MRI images can provide improved alignment compared to methods directly aligning DWI-derived scalar volumes, such as the widely used FA volumes. In the past, we established that our technique, using structural acquisitions, accurately and robustly aligns both cortical and non-cortical regions of the brain anatomy, and at present we demonstrate that when its results are applied to corresponding diffusion data, it also achieves superior accuracy. For our analysis, we compare the performance of CVS to the FA-FNIRT registration (registration component from the TBSS preprocessing) as well as linear alignment FLIRT [[18
]] computed from either the anatomical or the DWI data. We evaluate, both qualitatively and quantitatively, the resulting alignments by analyzing the correspondence of a set of manually labeled fiber bundles. The results demonstrate that there is a clear advantage to aligning the anatomy, when available, as opposed to relying only on the lower resolution, distorted diffusion information, even if the goal of the registration is the analysis of the diffusion properties of the brain.