title: Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI.
abstract: 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 cortical surface, which founds the multishape prediction process. Second, for temporally consistent fiber prediction, we propose to reliably estimate spatiotemporal connectivity features using low-rank tensor completion, thereby capturing the variability and richness of the temporal development of fibers. Experimental results confirm that the proposed variants significantly improve the prediction performance of our original multishape prediction 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 to predict the evolution of anatomical shapes with abnormal changes. Ultimately, devising accurate shape evolution prediction models that can help quantify and predict the severity of a brain disorder as it progresses will be of great aid in individualized treatment planning.
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA; CVIP, Computing, School of Science and Engineering, University of Dundee, UK.
authors.forename: Islem
authors.initials: I
authors.lastname: Rekik
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Geng
authors.initials: G
authors.lastname: Chen
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
authors.forename: Weili
authors.initials: W
authors.lastname: Lin
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea. Electronic address: dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000254:growth & development*; D038524:Diffusion Magnetic Resonance Imaging; D056324:Diffusion Tensor Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007223:Infant; D008137:Longitudinal Studies; D066127:White Matter / Q000033:anatomy & histology* / Q000254:growth & development*
keywords: Brain development; Heterogeneous-atlas estimation; Low-rank tensor completion; Multidirectional varifold; Multishape prediction