Super resolution reconstruction of diffusion tensor parameters from multi-oriented diffusion weighted images
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1
University of Antwerp, Physics, Belgium
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2
Erasmus Medical Center Rotterdam, Medical informatics and Radiology, Netherlands
Diffusion tensor imaging (DTI) is inherently a noisy imaging technique. Clinical time constraints prohibit the use of extensive averaging to increase the signal-to-noise ratio (SNR). To ensure that the SNR is sufficiently high, DTI acquisitions typically employ large voxels. As a result, many voxels consist of a mixture of signals from differently oriented structures, leading to partial volume effects and complicating DTI analysis.
In this work, we propose a super resolution reconstruction (SRR) method to improve the trade-off between spatial resolution, SNR, and acquisition time. High resolution (HR) diffusion tensor parameters are estimated directly from a set of diffusion weighted (DW) images with thick slices and different slice-orientations. We simultaneously take into account the spatial transformations arising from the slice orientations [1], motion correction, and the DTI-model. This makes our method much more flexible in terms of image acquisition requirements than existing SRR methods for DTI [2, 3]. Including the DTI model results in a non-linear regularized least squares problem that is solved using a trust-region Newton method. Reconstruction quality of the proposed SRR methodology was evaluated using both simulations and real data. The SRR result was compared with a direct HR acquisition, obtained in the same scan time.
Simulation experiments show a much lower mean squared error of fractional anisotropy and mean angular error of the first eigenvector for the proposed method compared to a direct HR acquisition. In real data, the reconstruction has a higher SNR than a direct HR acquisition, while still maintaining a good detail of the fine structures.
We have developed a SRR method specifically designed for DTI that enables HR investigation of the brain in clinically feasible scan time. It enables DTI with unprecedented resolution, minimizing the partial volume effects and thereby revealing finer structures.
Acknowledgements
This work was financially supported by the iMinds-SuperMRI project (Interdisciplinary Institute for Technology,
a research institute founded by the Flemish Government)
References
[1] D. H. J. Poot, V. Van Meir, and J. Sijbers, General and efficient super-resolution method for multi-slice MRI, 2010, MICCAI, 13(1), 615–22.
[2] D. H. J. Poot, B. Jeurissen, Y. Bastiaensen, J. Veraart, W. Van Hecke, P. M. Parizel, and J. Sijbers, Super-resolution for multislice diffusion tensor imaging, 2013, Magnetic Resonance in Medicine, 69(1)
[3] B. Scherrer, A. Gholipour, and S. K. Warfield, Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions., 2012, Medical image analysis, 16(7), 1465-1476
Keywords:
MRI,
diffusion MRI,
Diffusion Tensor Imaging,
super resolution,
Motion Correction
Conference:
Imaging the brain at different scales: How to integrate multi-scale structural information?, Antwerp, Belgium, 2 Sep - 6 Sep, 2013.
Presentation Type:
Poster presentation
Topic:
Poster session
Citation:
Van Steenkiste
G,
Jeurissen
B,
Poot
DH and
Sijbers
J
(2013). Super resolution reconstruction of diffusion tensor parameters from multi-oriented diffusion weighted images.
Front. Neuroinform.
Conference Abstract:
Imaging the brain at different scales: How to integrate multi-scale structural information?.
doi: 10.3389/conf.fninf.2013.10.00031
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Received:
31 Aug 2013;
Published Online:
31 Aug 2013.
*
Correspondence:
Miss. Gwendolyn Van Steenkiste, University of Antwerp, Physics, Antwerp, Belgium, gwendolyn.vansteenkiste@uantwerpen.be