Statistically assisted fluid image registration algorithm - SAFIRA

Caroline C. Brun, Natasha Lepore, Xavier Pennec, Yi-Yu Chou, Agatha D. Lee, Marina Barysheva, Greig I. de Zubicaray, Katie L. McMahon, Margaret J. Wright, Paul M. Thompson
2010 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in [2] and re-formulated using Lagrangian mechanics in [3]. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the non-statistical implementation. Covariance matrices for
more » ... both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the regularizing (i.e., the non-conservative Lagrangian) terms, creating four versions of the algorithm. We evaluated the accuracy of each algorithm variant using the manually labeled LPBA40 dataset, which provides us with ground truth anatomical segmentations. We also compared the power of the different algorithms using tensor-based morphometry -a technique to analyze local volumetric differences in brain structure-applied to 46 3D brain scans from healthy monozygotic twins.
doi:10.1109/isbi.2010.5490335 pmid:30555622 pmcid:PMC6291216 fatcat:7cwrewlsybdurpqofmpnoj7ohq