DT-REFinD: Diffusion Tensor Registration With Exact Finite-Strain Differential

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
IEEE transactions on medical imaging 07/2009; 28(12):1914-28. DOI: 10.1109/TMI.2009.2025654
Source: IEEE Xplore


In this paper, we propose the DT-REFinD algorithm for the diffeomorphic nonlinear registration of diffusion tensor images. Unlike scalar images, deforming tensor images requires choosing both a reorientation strategy and an interpolation scheme. Current diffusion tensor registration algorithms that use full tensor information face difficulties in computing the differential of the tensor reorientation strategy and consequently, these methods often approximate the gradient of the objective function. In the case of the finite-strain (FS) reorientation strategy, we borrow results from the pose estimation literature in computer vision to derive an analytical gradient of the registration objective function. By utilizing the closed-form gradient and the velocity field representation of one parameter subgroups of diffeomorphisms, the resulting registration algorithm is diffeomorphic and fast. We contrast the algorithm with a traditional FS alternative that ignores the reorientation in the gradient computation. We show that the exact gradient leads to significantly better registration at the cost of computation time. Independently of the choice of Euclidean or Log-Euclidean interpolation and sum of squared differences dissimilarity measure, the exact gradient achieves better alignment over an entire spectrum of deformation penalties. Alignment quality is assessed with a battery of metrics including tensor overlap, fractional anisotropy, inverse consistency and closeness to synthetic warps. The improvements persist even when a different reorientation scheme, preservation of principal directions, is used to apply the final deformations.

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    • ". To correct interscan motion, a fast non-rigid image registration algorithm [31], [32], [50], chosen for its accuracy and reproducibility in noisy cases, was applied to the multiple-directional and multiple TD DW images. The image registration was performed within MATLAB (R2010b, MathWorks, Inc., Natick, MA, USA). "
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    IEEE Transactions on Medical Imaging 09/2014; DOI:10.1109/TMI.2014.2356792 · 3.39 Impact Factor
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    • "Each time instance was considered as a dierent channel while the estimated transformation between successive channels was considered as constraint. Finally, the Demons framework was employed to register cortical surfaces parametrized as spheres in [57]. To generalize Demons on the sphere, a method was introduced to measure the distance between two transformations and to regularize the transformation. "
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    05/2013; 32(7). DOI:10.1109/TMI.2013.2265603
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    • "Dhollander et al. (2010) work can be seen as an extension of the PPD algorithm to their model of DW-MR datasets. However, as Zhang et al. (2006) and Alexander et al. (2001) indicate, there is little difference between using PPD or pure rigid rotation to register real DT-MR images and that is why it has been successfully used in the past (Zhang et al., 2006; Thomas Yeo et al., 2009), with a much lower computational cost. "
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