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This study systematically evaluates recurrent neural networks and Transformer models for diffusion MRI tractography, introducing a generation-validation training phase that enables supervision at the streamline level rather than just local steps. Testing on the ISMRM2015 challenge dataset, the researchers achieved the highest reported performance to date and demonstrated that their models work on real-world in vivo brain data. The work provides detailed analysis of how training data quality, including missing bundles and noisy streamlines, affects model performance.
Why it matters
Improved tractography methods could lead to better mapping of brain white matter connections, which is essential for understanding brain connectivity, planning neurosurgery, and studying neurological disorders. The systematic approach and practical recommendations provided will help other researchers develop more effective machine learning models for this challenging medical imaging task.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Machine learning (ML) has emerged as a promising approach for improving diffusion MRI (dMRI) tractography, a task that remains limited by the intrinsic tension between local diffusion information and global anatomical plausibility. In this work, we systematically evaluate recurrent neural networks (RNNs) and Transformer models for iterative tractography, with particular attention to training strategies, input representations (including convolutional neural network (CNN)-based embeddings and end-of-sequence (EOS) tokens), and hyperparameter selection. We introduce a generation-validation phase enabling supervision at the streamline level during training, allowing supervision despite the mismatch between local loss functions and global streamline quality. Using the ISMRM2015 tractography challenge dataset, our models achieve the highest reported performance to date. Through controlled experiments, we quantify the impact of missing bundles, noisy or imperfect training streamlines, and invalid fibers in the training set. Finally, we demonstrate the applicability of our best-performing models for in vivo data from the Tractoinferno database. Overall, our results highlight both the potential and the limits of sequence-based deep learning models such as Transformers and RNNs for tractography, and emphasize the need for improved phantoms and evaluation methods for in vivo validation. We provide takeaways and recommendations for future researchers training and validating sequence-based supervised methods for tractography.
Source: A foundation for systematic analysis of transformers and RNNs for tractography