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Abstract #2078

Higher Order Variational Denoising for Diffusion Tensor Imaging

Florian Knoll1, Tuomo Valkonen2, Kristian Bredies3, Rudolf Stollberger1

1Institute of Medical Engineering, Graz University of Technology, Graz, Austria; 2Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom; 3Department of Mathematics and Scientific Computing, University of Graz, Graz, Austria

High resolution diffusion weighted imaging and diffusion tensor imaging with isotropic voxels are desirable for a large number of applications. Unfortunately, acquisitions of such data sets are challenging, due to the notoriously low SNR of DWI. In addition, even with fast sequences like single shot EPI, measurement times often become prohibitively long because of the large number of diffusion encoding directions that have to be acquired. In this work we introduce two higher-order variational denoising approaches to reconstruct DTI data with isotropic voxels from a single average which are based on Total Generalised Variation.