3D MRI Image Pretraining through Controllable 2D Slice Navigation

Self-supervised pretraining has become the mainstream approach for learning MRI representations from unlabeled scans. However, most existing objectives treat each scan as static volumes, which is inefficient use of the scans. We ask whether there exists an intrinsic self-supervision signal that is different from reconstructing the masked patches to assist improving representation quality? Through transforming the 3D volumes into controllable 2D dynamic sequences by rendering slices at continuous positions, orientations, and scales, we extract the action trajectories as the advantageous signal. We study this formulation with an action-conditioned pretraining objective, where a tokenizer encodes slice observations and a latent dynamics model predicts the evolution of latent features. Across representative anatomical and spatial downstream tasks, the proposed pretraining is evaluated against other strong 3D MRI self-supervision or supervision baselines, and ablation modules of ours. These results suggest that controllable MRI slice navigation provides a useful complementary pretraining interface for learning anatomical and spatial representations from large unlabeled MRI collections.