Abstract #1964

Deep Learning-based noise reduction for advanced brain MR imaging: Application to quantitative biomarkers in brain tumors

Clement Debacker^1,2, Geoffroy Pouliquen^1,2, Sylvain Charron², Anna Fayolle^1,2, Valentin H. Prevost³, Wolter de Graaf⁴, Alexandre Roux^2,5, Johan Pallud^2,5, and Catherine Oppenheim^1,2

¹Radiology department, GHU Paris Psychiatrie et Neurosciences, Site Sainte-Anne, Paris, France, ²IMA-BRAIN, Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Paris, France, ³Canon Medical Systems Corporation, Tochigi, Japan, ⁴Canon Medical Systems Europe, Zoetermeer, Netherlands, ⁵Neuro-surgery department, GHU Paris Psychiatrie et Neurosciences, Site Sainte-Anne, Paris, France

Synopsis

Keywords: Tumors, TumorOur work is the clinical validation of a deep-learning algorithm (DLR) used to denoise MR images on quantitative MR biomarkers. Since it has been trained on including T1- and T2-weighted conventional images, in healthy volunteers, its effects on multiparametric quantitative MRI in patients, are uncertain. It could potentially improve brain tumors characterization by providing quantitative biomarkers under clinical time-constraints.

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