发表机构
Université de Sherbrooke; Mila, Université de Montréal; Imeka Solutions Inc（舍布鲁克大学; 蒙特利尔大学米拉研究所; 伊梅卡解决方案公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究系统评估RNN和Transformer在纤维束成像中的性能，引入生成-验证阶段实现流线级监督，在ISMRM2015数据集上达到最高性能，并量化了训练数据缺陷的影响。
AI 中文摘要
机器学习（ML）已成为改善扩散磁共振成像（dMRI）纤维束成像的一种有前景的方法，而这一任务仍受限于局部扩散信息与全局解剖合理性之间的固有矛盾。在本工作中，我们系统性地评估了循环神经网络（RNN）和Transformer模型在迭代纤维束成像中的应用，特别关注训练策略、输入表示（包括基于卷积神经网络（CNN）的嵌入和序列结束（EOS）标记）以及超参数选择。我们引入了一个生成-验证阶段，使得在训练期间能够在流线层面进行监督，从而在局部损失函数与全局流线质量不匹配的情况下实现监督。使用ISMRM2015纤维束成像挑战数据集，我们的模型达到了迄今报告的最高性能。通过受控实验，我们量化了缺失纤维束、噪声或不完善的训练流线以及训练集中无效纤维的影响。最后，我们展示了我们性能最佳的模型在来自Tractoinferno数据库的体内数据上的适用性。总体而言，我们的结果突显了基于序列的深度学习模型（如Transformer和RNN）在纤维束成像中的潜力与局限，并强调了改进体模和评估方法以进行体内验证的必要性。我们为未来研究人员训练和验证基于序列的监督方法用于纤维束成像提供了要点和建议。
英文摘要
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.
