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Abstract #2796

Real Diffusion Weighted MRI Enabling True Signal Averaging and Increased Diffusion Contrast

Cornelius Eichner 1,2 , Stephen F Cauley 1 , Julien Cohen-Adad 3 , Harald E Mller 2 , Robert Turner 2 , Kawin Setsompop 1 , and Lawrence L Wald 1

1 Martinos Center for Biomedical Imaging, Boston, MA, United States, 2 Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, SX, Germany, 3 cole Polytechnique, University of Montreal, Montreal, QC, Canada

This project aims to remove the noise floor, induced by a Rician noise distribution of magnitude data, in diffusion-weighted imaging with low SNR. We implemented a rephasing algorithm to extract real valued diffusion images from complex datasets. Phase corrected real valued data and traditional magnitude data were analyzed regarding signal averaging, model fitting and ability to resolve crossing fibers. Our results reveal that rephased real valued data eliminate Rician noise bias and, therefore, enable unbiased averaging and diffusion model fitting. For future diffusion applications, this method will help to acquire diffusion data with higher resolutions and/or stronger diffusion weightings.

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