Abstract #0466

Magnitude versus complex-valued images for spinal cord diffusion MRI: which one is best?

Francesco Grussu^1,2, Jelle Veraart³, Marco Battiston¹, Torben Schneider⁴, Julien Cohen-Adad^5,6, Manuel Jorge Cardoso^7,8, Claudia Angela Gandini Wheeler-Kingshott^1,9,10, Els Fieremans³, Daniel C. Alexander², and Dmitry S. Novikov³

¹Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, ²Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, ³Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, United States, ⁴Philips UK, Guildford, Surrey, United Kingdom, ⁵NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada, ⁶Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, QC, Canada, ⁷Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ⁸Dementia Research Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, ⁹Brain MRI 3T Research Centre, C. Mondino National Neurological Institute, Pavia, Italy, ¹⁰Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy

Advanced diffusion imaging of the spinal cord is hampered by low signal-to-noise ratio, leading to strong Rician bias in magnitude images. Here, we investigate how to mitigate such bias studying complex-valued 3T diffusion scans of the cervical cord. We test two approaches, based on decorrelated phase (DP) and total variation (TV) filtering, corroborating results with simulations. The DP and TV methods, proposed for the brain, can be applied successfully also in the cord. Moreover, they appear useful pre-processing tools for image denoising, as state-of-the-art noise removal based on Marčenko-Pastur principal component analysis (MP-PCA) performs better on complex-valued as opposed to magnitude data.

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