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Abstract #3350

Complex-valued diffusion MRI data processing: Application to neural soma imaging

Enrico Kaden1, Umesh S Rudrapatna2, Noemi G Gyori1,3, Uran Ferizi4, Chris A Clark3, Derek K Jones2,5, and Daniel C Alexander1

1Centre for Medical Image Computing, University College London, London, United Kingdom, 2Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom, 3Great Ormond Street Institute of Child Health, University College London, London, United Kingdom, 4Department of Radiology, New York University School of Medicine, New York, NY, United States, 5School of Psychology, Australian Catholic University, Melbourne, Australia

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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