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Abstract #2752

A unified signal readout improves denoising of multi-modal spinal cord MRI

Francesco Grussu1,2, Marco Battiston1, Jelle Veraart3, Torben Schneider4, Julien Cohen-Adad5,6, Manuel Jorge Cardoso7,8, Daniel C. Alexander2, Dmitry S. Novikov3, Els Fieremans3, and Claudia Angela Gandini Wheeler-Kingshott1,9,10

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

Denoising based on Marčenko-Pastur principal component analysis (MP-PCA) is a versatile model-free method proposed for brain imaging. Here, we assess the potential of the technique for multi-modal quantitative spinal cord MRI. We analyse a unique data set consisting of multi-modal cervical scans obtained with a unified signal readout, and corroborate in vivo findings with simulations. We show that MP-PCA denoising is a valid tool for pre-processing a variety of signal contrasts in the spinal cord. In particular, the overall performance of denoising can be enhanced further on multi-modal acquisitions with matched signal readout, due to increased data redundancy.

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