Diffusion tensor image up-sampling: a registration-based approach |
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| Authors: | Zhenhua Mai Marleen Verhoye Annemie Van der Linden Jan Sijbers |
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| Affiliation: | 1. Center of Functionally Integrative Neuroscience, Aarhus University Hospital, Aarhus, Denmark.;2. Translational Neuropsychiatry Unit, Aarhus University, Risskov, Denmark.;3. Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, United States.;4. Stereology and Electron Microscopy Laboratory, Centre for Stochastic Geometry and Advanced Bioimaging, Aarhus University, Aarhus, Denmark.;5. Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.;1. Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada;2. Department of Radiology, Stanford University, Stanford, CA, United States;3. A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States;4. NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Institute of Neurology, London, London, United Kingdom;5. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States;6. Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada |
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| Abstract: | Diffusion weighted images (DWI), from which the corresponding diffusion tensor images (DTI) are estimated, are commonly acquired with anisotropic discretizations. Traditional methods to up-sample diffusion weighted images generally rely on scene-based interpolation and do not exploit structural information from the images. In this study, a DTI up-sampling framework is presented that incorporates the underlying anatomical shape information by means of non-rigid inter-slice registration. A strategy is proposed to reorient the interpolated tensor in order to maintain its proper orientation. Tests on phantom as well as on real data sets show that the proposed method is able to produce better results compared to scene based interpolation methods in terms of the accuracy of DWI/DTI interpolation, especially when diffusion tensor orientation is taken into account. |
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