Microscopic diffusion anisotropy imaging requires averaging the diffusion signal over the gradient directions to regress out the unwanted effects of the fibre orientation distribution. However, Rician noise biases the mean signal calculations especially in the high b-value regime and subsequently the estimation of microstructural tissue features. In this work we develop new data processing methods using complex-valued MRI data that remove the background phase and hence retain the Gaussian characteristics of the signal noise, which is demonstrated in neural soma imaging, a novel application of the Spherical Mean Technique (SMT).
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