Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning.

ABSTRACT: The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of l

SUBMITTER: Peng L 

PROVIDER: S-EPMC8458966 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature