Automated quality control in MR-based brain morphometry
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.