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DT-REFinD Code Release

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Description

Diffusion Tensor Registration with Exact Finite Strain Differential (DT-REFIND) is an algorithm that registers diffusion tensor images using full tensor information. Deforming tensor images require us to interpolate AND reorient the tensors in order to be consistent with anatomy. Unfortunately, this reorientation process introduces difficulty in computing the gradient of the objective function. 

In this work, we compute an exact differential of the Finite-Strain reorientation strategy. 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. Implemented within the Insight Toolkit (ITK) framework, registration of a pair of 128x128x60 diffusion tensor volumes takes 15 minutes, which is faster than many non-linear scalar image registration algorithms. When compared with a traditional alternative that does not take into account the reorientation in the gradient computation, we show that using the exact gradient achieves significantly better registration.

Publications

  • B.T.T. Yeo, T. Vercauteren, P. Fillard, J-M. Peyrat, X. Pennec, P. Golland, N. Ayache, O. Clatz. DT-REFinD: Diffusion Tensor Registration with Exact Finite-Strain Differential. IEEE Transactions on Medical Imaging, 28(12):1914--1928, 2009. [pdf]
  • B.T.T. Yeo, T. Vercauteren, P. Fillard, X. Pennec, P. Golland, N. Ayache, O. Clatz. DTI Registration with Exact Finite-Strain Differential. Proceedings of the International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), 700--703, 2008. [pdf] ISBI Travel Grant

Code Release

The code is currently incorporated into the freely-available MedINRIA.  MedINRIA is a graphical interface that allows the visualization and processing of medical imaging data. 

Thanks to Pierre Fillard and Andrew Sweet, the source code is now available here

Acknowledgments

This research is funded by the INRIA “associated teams” program CompuTumor. B.T. Thomas Yeo is funded by the Agency for Science, Technology and Research, Singapore.
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