Current denoising methods for diffusion weighted images can obtain high quality estimates of local fiber orientation in static structures. However, recovering reliable fiber orientation from in vivo data is considerably more difficult. To address this problem we use a geometric approach, with a spatio-temporal Cartan frame field to model spatial (within time-frame) and temporal (between time-frame) rotations within a single consistent mathematical framework. The key idea is to calculate the Cartan structural connection parameters, and then fit probability distributions to these volumetric scalar fields. Voxels with low log-likelihood with respect to these distributions signal geometrical "noise" or outliers. With experiments on both simulated (canine) moving fiber data and on an in vivo human heart sequence, we demonstrate the promise of this approach for outlier detection and denoising via inpainting.
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