Bndicte Marchal1, Tobias Kober1, Tom Hilbert2, Delphine Ribes3, Nicolas Chevrey4, Alexis Roche1, Jean-Philippe Thiran5, Reto Meuli4, Gunnar Krueger1
1Advanced Clinical Imaging Technology, Siemens Healthcare Sector - CIBM, Renens, Switzerland; 2Universitt Heidelberg, Germany; 3ARTORG Center for Computer Aided Surgery, Univ. of Bern, Switzerland; 4Centre Hospitalier Universitaire Vaudois and Univ. of Lausanne; 5Signal Processing Laboratory (LTS5) Ecole Polytechnique Fdrale de Lausanne
Normal aging and a wide range of neurologic, inflammatory or psychiatric diseases lead to changes in the brain tissue over time. In the interest of diagnosis, prognosis and treatment monitoring, it is highly desirable to have robust tools that reliably measure brain morphometry. We explore the ability of an automated MR image quality assessment technique to predict the accuracy of subsequent algorithms for brain quantitative analysis. The approach proofs to be a very promising candidate to objectively assess quality prior to any post-processing in order to attribute tissue changes to a potential pathology rather than to image degradation.