Improving DTI Resolution from a Single Clinical Acquisition: A Statistical Approach using Spatial Prior

Vikash Gupta 1 Nicholas Ayache 1 Xavier Pennec 1
1 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Di ffusion Tensor Imaging (DTI) provides us with valuable information about the white matter fibers and their arrangement in the brain. However, clinical DTI acquisitions are often low resolution, causing partial volume eff ects. In this paper, we propose a new high resolution tensor estimation method. This method makes use of the spatial correlation between neighboring voxels. Unlike some super-resolution algorithms, the proposed method does not require multiple acquisitions, thus it is better suited for clinical situations. The method relies on a maximum likelihood strategy for tensor estimation to optimally account for the noise and an anisotropic regularization prior to promote smoothness in homogeneous areas while respecting the edges. To the best of our knowledge, this is the fi rst method to produce high resolution tensor images from a single low resolution acquisition. We demonstrate the effi ciency of the method on synthetic low-resolution data and real clinical data. The results show statistically signifi cant improvements in fiber tractography.
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Communication dans un congrès
Kensaku Mori and Ichiro Sakuma and Yoshinobu Sato and Christian Barillot and Nassir Navab. Proceedings of Medical Image Computing and Computer Assisted Intervention 2013 (MICCAI), Sep 2013, Nagoya, Japan. Springer, 8151, pp.477-484, 2013, Lecture Notes in Computer Science - LNCS. 〈10.1007/978-3-642-40760-4_60〉
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Vikash Gupta, Nicholas Ayache, Xavier Pennec. Improving DTI Resolution from a Single Clinical Acquisition: A Statistical Approach using Spatial Prior. Kensaku Mori and Ichiro Sakuma and Yoshinobu Sato and Christian Barillot and Nassir Navab. Proceedings of Medical Image Computing and Computer Assisted Intervention 2013 (MICCAI), Sep 2013, Nagoya, Japan. Springer, 8151, pp.477-484, 2013, Lecture Notes in Computer Science - LNCS. 〈10.1007/978-3-642-40760-4_60〉. 〈hal-00845927〉

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