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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