Can We Distinguish Intra- and Inter-Variability with Log Jacobian Maps Derived from Brain Morphological Deformations Using Pediatric MRI Scans?

Motivation: Importance of analyzing deformation fields derived from both intra- and inter-individual pairs of T1-weighted images which could offer insights into typical and atypical neurodevelopment. Goal(s): We aimed to fine-tune a 3D CNN to classify intra and inter-individual variability based on log Jacobian maps from deformation fields of pediatric longitudinal MRI. Approach: 279 log Jacobian maps of both intra- and inter-individual pairs are extracted using ANTs. A 3D CNN is trained in two ways (overlap and no overlap) for binary classification using 10-fold cross-validation. Results: As expected, the overlap scenario had higher accuracy and F1 score compared to no-overlap, nonetheless both achieving good results. Impact: This project's focus on pediatric MRI scans aims to understand deformations in medical imaging, advancing diagnostic tools. By distinguishing intra and inter-individual variability using log Jacobian-derived deformation patterns, it subsequently aims to model typical neurodevelopment through trajectories for deviation prediction.