sf-pediatric: A robust and age-adaptable end-to-end pipeline for pediatric diffusion MRI

Abstract Diffusion MRI (dMRI) provides a powerful, non-invasive window into white matter (WM) development. Yet, most existing processing pipelines are not well-suited to the rapidly evolving neurophysiology of the pediatric brain. Here, we introduce sf-pediatric, a scalable, end-to-end, age-adaptable dMRI pipeline that integrates normative models of brain diffusivities to enable optimal subject-specific analysis from birth through 18 years old. Leveraging normative trajectories derived from nearly 2,000 participants from six cohorts, sf-pediatric dynamically calibrates diffusion priors, template selection, segmentation, and WM atlases based on the subject’s age. By incorporating automatic quality control into a portable, tested, containerized, open-access, and press-button framework across computing environments, sf-pediatric provides a robust pipeline for large-scale pediatric dMRI studies. We validated this approach by showing improved local modeling and cortical fanning while preserving reproducibility and the ability to derive brain-behavior relationships. Additionally, we demonstrated robust recovery of known developmental trajectories of WM microstructure and connectome-derived network organization.