DT-REFinD: Diffusion Tensor Registration With Exact Finite-Strain Differential
Name
Yeo-2009-DT-REFinD_ Diffusion.pdf
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Author(s)
Golland, Polina
Yeo, Boon Thye Thomas
Vercauteren, Tom
Fillard, Pierre
Peyrat, Jean-Marc
Pennec, Xavier
Ayache, Nicholas
Clatz, Olivier
Date Issued
November 2009
Journal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers
Citation
Yeo, B.T.T. et al. “DT-REFinD: Diffusion Tensor Registration With Exact Finite-Strain Differential.” Medical Imaging, IEEE Transactions on 28.12 (2009): 1914-1928. © 2010 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
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.
Subjects
DT-REFinD algorithm
Euclidean interpolation
Log-Euclidean interpolation
closed-form gradient
computer vision
diffeomorphic nonlinear registration
diffusion tensor image registration
finite-strain differential algorithm
fractional anisotropy
inverse consistency
one-parameter subgroups
pose estimation literature
registration objective function
synthetic warps
tensor overlap
tensor reorientation strategy
velocity field representation
Diffeomorphisms
diffusion tensor imaging
finite-strain (FS)
finite-strain differential
preservation of principal directions
registration
tensor reorientation
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/TMI.2009.2025654
