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Abstract #4664

Methods for Reorienting and Retransforming Diffusion Weighted Imaging Data

Thijs Dhollander1,2, Wim Van Hecke1,3, Frederik Maes1,2, Stefan Sunaert1,3, Paul Suetens,,2

1Medical Imaging Research Center (MIRC), K.U.Leuven, Leuven, Belgium; 2Department of Electrical Engineering (ESAT), K.U.Leuven, Leuven, Belgium; 3Department of Radiology, University Hospitals of the K.U.Leuven, Leuven, Belgium

In the context of registration algorithms, the application of spatial transformations to images is crucial. This poses a challenge of its own for diffusion weighted imaging (DWI) data, since the information in every voxel is dependent on the angular structure of the underlying tissue. After interpolation, an extra reorientation step to correct the data in each voxel is necessary. We review different reorientation strategies, starting from the basic methods that operate on the tensor from diffusion tensor imaging (DTI) and building up to full fiber orientation distribution function (fODF) retransformation and methods that work on the raw data in q-space.