Multi-Atlas Tensor-Based Morphometry and its Application to a Genetic Study of 92 Twins

Here we develop a multi-template analysis for tensor-based morphometry (TBM) which aims to reduce error from the registration step. In conventional TBM, a single template is nonlinearly registered to all images in the study, and the deformation field statistics are computed from the transformations. Using an MRI dataset from 23 monozygotic and 23 dizygotic twin pairs, we instead registered each individual twin image to 9 additional brain templates using a Riemannian fluid algorithm [3]. Average deformation tensors from multiple registrations were computed within each image, using a log-Euclidean framework [1]. To quantify improvements as the number of registration templates increased from 1 to 9, sequential t-tests assessed the significance of any error reduction, as each new template was added. For each number of templates, we also computed two tensor-derived metrics, and maps of the intraclass correlation of local volume differences, to evaluate any power advantages of multi-atlas TBM.