Abstract #2409

AUTOMATED DETECTION OF WHITE-MATTER AND CORTICAL LESIONS IN MP2RAGE AT ULTRA-HIGH FIELD USING A SINGLE SCAN

Mário João Fartaria ^1,2,3, Alexis Roche^1,2,3, Alexandra Şorega⁴, Kieran O'Brien^5,6, Gunnar Krueger⁷, Bénédicte Maréchal^1,2,3, Pascal Sati⁸, Daniel S. Reich⁸, Tobias Kober^1,2,3, Meritxell Bach Cuadra^2,3,9, and Cristina Granziera^10,11

¹Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, ²Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, ³Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ⁴Department of Radiology, Valais Hospital, Sion, Switzerland, ⁵Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, QC, Australia, ⁶6. Siemens Healthcare Pty Ltd., Brisbane, Queensland, Australia, ⁷Siemens Medical Solutions USA, Boston, MA, United States, ⁸Translational Neuroradiology Section, Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), Bethesda, MD, United States, ⁹Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland, ¹⁰Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, ¹¹Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

Ultra-high-field Magnetic Resonance Imaging (7T MRI) has been shown to be a valuable tool to assess focal and diffuse pathology in multiple sclerosis (MS) patients, both in grey- and in white-matter. In this work, we developed and evaluated a method to automatically assess MS lesion load using magnetization-prepared two inversion-contrast rapid gradient-echo (MP2RAGE) MRI at 7T. The validation was conducted in a cohort of twenty MS patients from two research centers through a ground truth based on manual segmentations performed by a radiologist. Our single-sequence segmentation accurately detects visible white-matter and cortical lesions.

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