Abstract #2071

De-noising of diffusion-weighted MRI data by averaging of inconsistent input data in wavelet space

Henrik Marschner¹, Cornelius Eichner¹, Alfred Anwander¹, André Pampel¹, and Harald E. Möller¹

¹Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Diffusion Weighted Images datasets with high spatial resolution and strong diffusion weighting are often deteriorated with low SNR. Here, we demonstrate the feasibility of a recently presented repetition-free averaging based de-noising (AWESOME). That technique reduces noise by averaging over a series of N images with varying contrast in wavelet space and regains intensities and object features initially covered by noise. We show that high resolution DWIs are achievable in a quality that almost equals to that obtained from 6fold complex averaging.

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