Joint Prediction of Longitudinal Development of Cortical Surfaces andWhite Matter Fibers from Neonatal MRI

摘要

The human brain can be modeled as multiple interrelated shapes (or a multishape), each for characterizing one aspect of the brain, such as the cortex and white matter pathways. Predicting the developing multishape is a very challenging task due to the contrasting nature of the developmental trajectories of the constituent shapes: smooth for the cortical surface and non-smooth for white matter tracts due to changes such as bifurcation. We recently addressed this problem and proposed an approach for predicting the multishape developmental spatiotemporal trajectories of infant brains based only on neonatal MRI data using a set of geometric, dynamic, and fiber-to-surface connectivity features. In this paper, we propose two key innovations to further improve the prediction of multishape evolution. First, for a more accurate cortical surface prediction, instead of simply relying on one neonatal atlas to guide the prediction of the multishape, we propose to use multiple neonatal atlases to build a spatially heterogeneous atlas using the multidirectional varifold representation. This individualizes the atlas by locally maximizing its similarity to the testing baseline cortical shape for each cortical region, thereby better representing the baseline testing corticalsurface, which founds the multishape prediction process. Second, for temporallyconsistent fiber prediction, we propose to reliably estimatespatiotemporal connectivity features using low-rank tensorcompletion, thereby capturing the variability and richness of the temporaldevelopment of fibers. Experimental results confirm that the proposed variantssignificantly improve the prediction performance of our original multishapeprediction framework for both cortical surfaces and fiber tracts shape at 3, 6,and 9 months of age. Our pioneering model will pave the way for learning how topredict the evolution of anatomical shapes with abnormal changes. Ultimately,devising accurate shape evolution prediction models that can help quantify andpredict the severity of a brain disorder as it progresses will be of great aidin individualized treatment planning.