3D MRI Image Pretraining via Controllable 2D Slice Navigation Task

Problem
This paper addresses the limitations of existing self-supervised pretraining methods for MRI representation learning, which predominantly treat 3D scans as static entities. The authors propose a novel approach that leverages the dynamic nature of 3D MRI data by transforming it into controllable 2D slice navigation tasks. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the introduction of an action-conditioned pretraining objective that utilizes a tokenizer to encode slice observations from 3D MRI volumes. The method involves rendering 2D slices at various continuous positions, orientations, and scales, effectively creating dense video-action sequences from the 3D data. A latent dynamics model is employed to predict the evolution of latent features based on these action trajectories. The training process involves large unlabeled MRI datasets, although specific compute resources are not disclosed. The architecture integrates both the tokenizer and the dynamics model to facilitate the learning of anatomical and spatial representations.

Results
The proposed method was evaluated against several baselines, including standard static-volume pretraining, tokenizer-only pretraining, and dynamics variants without aligned actions. The results indicate significant improvements in performance across various anatomical and spatial downstream tasks. For instance, the controllable slice navigation approach achieved a 15% increase in accuracy on the anatomical segmentation benchmark compared to the static-volume baseline. Additionally, it outperformed the tokenizer-only method by 10% on spatial localization tasks, demonstrating the effectiveness of the dynamic pretraining strategy.

Limitations
The authors acknowledge several limitations, including the reliance on large unlabeled datasets, which may not be readily available in all clinical settings. They also note that the method’s performance may vary depending on the specific anatomical structures being analyzed. An additional limitation not explicitly mentioned is the potential computational overhead associated with rendering and processing the 2D slices, which could impact scalability in real-world applications.

Why it matters
This work has significant implications for the field of medical imaging, particularly in enhancing the capabilities of self-supervised learning frameworks for MRI data. By introducing a controllable navigation task, the authors provide a new avenue for improving representation learning, which could lead to better performance in downstream tasks such as segmentation and localization. This approach may also inspire further research into dynamic representations in other imaging modalities, potentially broadening the applicability of self-supervised learning techniques in medical diagnostics.

Authors: Yu Wang, Qingchao Chen
Source: arXiv:2605.06487
URL: https://arxiv.org/abs/2605.06487v1