Abstract #2752

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

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

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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