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Abstract #0668

Deriving Brain Myelin Water Fraction Maps from Relaxometry: a Data-Driven Approach

Gian Franco Piredda1,2,3, Tom Hilbert1,2,3, Jonas Richiardi1,2, Erick J. Canales-Rodríguez2, Marco Pizzolato3, Reto Meuli2, Jean-Philippe Thiran2,3, and Tobias Kober1,2,3

1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland, 3LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Currently, one of the gold-standard methods to obtain brain myelin water fraction (MWF) maps is the multi-echo spin-echo sequence. To overcome some of its limitations (e.g. long acquisition times), a data-driven approach for deriving MWF maps is proposed here. A general linear model (GLM) and a conditional generative adversarial network (cGAN) were trained to learn the reference MWF from T1 and T2 maps acquired in a healthy cohort. While GLM-derived maps exhibited MWF overestimation, especially in WM tissue, the cGAN yielded images in agreement with the reference. The proposed methods were preliminarily tested in patients and revealed myelin degradation in expected areas.

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