Exploring the robustness of TractOracle methods in RL-based tractography
This work addresses the problem of improving robustness and reliability in brain white matter tractography for medical imaging researchers, though it is incremental as it builds on existing RL-based methods.
The paper investigated extensions of the TractOracle-RL reinforcement learning framework for brain tractography, introducing Iterative Reward Training (IRT) to refine anatomical guidance without human input, and found that RL methods with oracle feedback consistently outperformed traditional techniques across five datasets in accuracy and anatomical validity.
Tractography algorithms leverage diffusion MRI to reconstruct the fibrous architecture of the brain's white matter. Among machine learning approaches, reinforcement learning (RL) has emerged as a promising framework for tractography, outperforming traditional methods in several key aspects. TractOracle-RL, a recent RL-based approach, reduces false positives by incorporating anatomical priors into the training process via a reward-based mechanism. In this paper, we investigate four extensions of the original TractOracle-RL framework by integrating recent advances in RL, and we evaluate their performance across five diverse diffusion MRI datasets. Results demonstrate that combining an oracle with the RL framework consistently leads to robust and reliable tractography, regardless of the specific method or dataset used. We also introduce a novel RL training scheme called Iterative Reward Training (IRT), inspired by the Reinforcement Learning from Human Feedback (RLHF) paradigm. Instead of relying on human input, IRT leverages bundle filtering methods to iteratively refine the oracle's guidance throughout training. Experimental results show that RL methods trained with oracle feedback significantly outperform widely used tractography techniques in terms of accuracy and anatomical validity.