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Abstract #0730

BigBrain-MR: a computational phantom for ultra-high-resolution MR methods development

Cristina Sainz Martinez1,2, Mathieu Lemay1, Meritxell Bach Cuadra2,3,4, and João Jorge1,2
1Systems Division, Swiss Center for Electronics and Microtechnology (CSEM), Nêuchatel, Switzerland, 2Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, 3Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 4Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

With the increasing importance of ultra-high field systems, suitable simulation platforms are needed for the development of high-resolution imaging methods. Here, we propose a realistic computational brain phantom at 100μm resolution, by mapping fundamental MR properties (e.g., T1, T2, coil sensitivities) from existing brain MRI data to the fine-scale anatomical space of BigBrain, a publicly-available 100μm-resolution ex-vivo image obtained with optical methods. We propose an approach to map image contrast from lower-resolution MRI data to BigBrain, retaining the latter’s fine structural detail. We then show its value for methodological development in two applications: super-resolution, and reconstruction of highly-undersampled k-space acquisitions.

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