基于深度学习的扩散MRI纤维束追踪：整合空间与解剖信息
Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information
AI总结：
针对扩散MRI纤维束追踪中假阳性连接过多的问题，提出整合图像域空间与解剖信息的深度学习框架，有效提升了白质覆盖率与追踪准确性。
摘要：
扩散MRI纤维束追踪技术实现了大脑白质通路的非侵入性可视化。通过促进大脑连接和神经系统疾病的研究，它在神经科学和临床领域发挥着至关重要的作用。然而，重建纤维束图的准确性一直是一个长期存在的挑战。最近，深度学习方法被应用于改善纤维束图以获得更好的白质覆盖率，但通常以产生过多的假阳性连接为代价。这在很大程度上是因为它们依赖局部信息来预测长程纤维束。为了提高纤维束传播预测的准确性，我们引入了一种新颖的深度学习框架，该框架整合了图像域空间信息和沿纤维束的解剖信息，前者通过卷积层提取，后者通过Transformer-decoder建模。此外，我们采用加权损失函数来解决训练过程中遇到的纤维类别不平衡问题。我们在模拟的ISMRM 2015 Tractography Challenge数据集上评估了所提出的方法，实现了66.2%的有效纤维束率、63.8%的白质覆盖率，并成功重建了25个纤维束中的24个。此外，在多站点Tractoinferno数据集上，所提出的方法展示了其处理各种扩散MRI采集方案的能力，与基于RNN的方法相比，白质覆盖率提高了5.7%，过度延伸减少了4.1%。
英文摘要：
Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractograms has been a longstanding challenge. Recently, deep learning methods have been applied to improve tractograms for better white matter coverage, but often comes at the expense of generating excessive false-positive connections. This is largely due to their reliance on local information to predict long range streamlines. To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the later modeled via a Transformer-decoder. Additionally, we employ a weighted loss function to address fiber class imbalance encountered during training. We evaluate the proposed method on the simulated ISMRM 2015 Tractography Challenge dataset, achieving a valid streamline rate of 66.2%, white matter coverage of 63.8%, and successfully reconstructing 24 out of 25 bundles. Furthermore, on the multi-site Tractoinferno dataset, the proposed method demonstrates its ability to handle various diffusion MRI acquisition schemes, achieving a 5.7% increase in white matter coverage and a 4.1% decrease in overreach compared to RNN-based methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
