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COMPUTATIONAL ANATOMYWe examined the reproducibility and the power to detect real changes of different computational techniques. It is the first work to systematically investigate the reproducibility and variability of different registration methods in tensor-based morphometry (TBM). In particular, we compared matching functionals (sum of squared differences (L2) and mutual information (MI)), as well as large deformation registration schemes (unbiased registration and viscous fluid registration) using serial MRI scans of ten normal elderly patients from the preparatory phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI) and ten Alzheimer's subjects from the ADNI follow-up phase. Our results show that the unbiased methods have higher reproducibility. The unbiased methods are less likely to produce changes in the absence of any real physiological change. Moreover, they are also better in detecting biological deformations by penalizing any bias in the corresponding statistical maps. FOLLOW-UP SCANS
BASELINE SCANS
INVERSE CONSISTENCY OF UNBIASED REGISTRATION
UNBIASED REGISTRATION FOR DETECTING ALZHEIMER'S DISEASE
UNBIASED NONLINEAR ELASTIC REGISTRATION We propose a new nonlinear image registration model which is based on nonlinear elastic regularization and unbiased registration. The unbiased large-deformation nonlinear elasticity method was tested using volumetric serial magnetic resonance images and shown to have some advantages for medical imaging applications.
INVERSE CONSISTENCY OF UNBIASED NONLINEAR ELASTIC REGISTRATION
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Igor Yanovsky |