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Abstract #4901

Robust myelin water imaging from multi-echo T2 data using second-order Tikhonov regularization with control points

Erick Jorge Canales-Rodriguez1,2, Marco Pizzolato3, Gian Franco Piredda1,3,4, Tom Hilbert1,3,4, Kunz Nicolas5, Tobias Kober1,3,4, Jean-Philippe Thiran1,3, Caroline Pot6,7, and Alessandro Daducci1,3,8

1Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland, 2FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain, 3LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 5Centre d'Imagerie BioMédicale (CIBM)-AIT, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland, 6Department of Pathology and Immunology, Geneva University Hospital and University of Geneva, Geneva, Switzerland, 7Division of Neurology and Neuroscience Research Center, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland, 8Computer Science Department, University of Verona, Verona, Italy

Myelin water imaging is an MRI technique used to quantify myelination in the brain. The state-of-the-art reconstruction method is based on non-negative least squares optimization with zero-order Tikhonov regularization. In this study, a second-order Tikhonov regularization approach with control points was examined. This penalty term is more efficient for promoting smooth solutions while minimizing the contamination between myelin and non-myelin components. The performance of the proposed algorithm was investigated on in-vivo and ex-vivo multi-echo T2 data. It exhibited a higher correlation with histology than the state-of-the-art method. Its stability was studied using scan-rescan data.

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