Meritxell Bach Cuadra1,
2, Sebastien Gelin, 23, Alexis Roche1, 4,
Oscar Esteban, 25, Tobias Kober4, Jos P. Marques6,
Cristina Granziera7, Gunnar Krger4, 8
1Radiology
Department, University Hospital Center (CHUV) and University of Lausanne
(UNIL), Lausanne, Vaud, Switzerland; 2Signal Processing Laboratory
(LTS5), Ecole Polytechnique Fdrale de Lausanne (EPFL), Lausanne, Vaud,
Switzerland; 3Bern University, Bern, Switzerland; 4Advanced
Clinical Imaging Technology, Siemens Healthcare Sector IM&WS S, Lausanne,
Vaud, Switzerland; 5Biomedical Image Technologies (BIT),
Universidad Politcnica de Madrid, Madrid, Spain; 6CIBM-Animal
Imaging and Technology core, University of Lausanne, Lausanne, Vaud,
Switzerland; 7Department of Clinical Neurosciences, University
Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, Vaud,
Switzerland; 8CIBM-AIT, Ecole Polytechnique Fdrale de Lausanne
(EPFL), Lausanne, Vaud, Switzerland
Existing brain tissue segmentation methods are optimized for conventional T1-weighted images such as MPRAGE. However, recent clinical research has highlighted the benefits of other image acquisition techniques such as MP2RAGE. In this work, we study the ability of three state-of-the-art algorithms to automatically segment WM, GM and CSF in MP2RAGE imaging. We quantify the differences between MPRAGE and MP2RAGE-based brain tissue probability maps through statistical voxel-based analysis. Results on a group of 19 healthy subjects show significant statistical differences between GM probability maps in the central nuclei and the cerebellum, for each of the three tested methods.
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