Abstract #4901

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

Erick Jorge Canales-Rodriguez^1,2, Marco Pizzolato³, Gian Franco Piredda^1,3,4, Tom Hilbert^1,3,4, Kunz Nicolas⁵, Tobias Kober^1,3,4, Jean-Philippe Thiran^1,3, Caroline Pot^6,7, and Alessandro Daducci^1,3,8

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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