Yang Yiqiong, Yuan Yitian, Ren Baoxing, Wu Ye, Feng Yanqiu, Zhang Xinyuan
School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China.
Neuroimage. 2025 Aug 15;317:121314. doi: 10.1016/j.neuroimage.2025.121314. Epub 2025 Jun 25.
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 latter 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.
扩散磁共振成像纤维束示踪技术能够对大脑中的白质通路进行无创可视化。它通过促进对脑连接性和神经疾病的研究，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，深度学习方法已被应用于改进纤维束图，以实现更好的白质覆盖，但往往以产生过多的假阳性连接为代价。这主要是由于它们依赖局部信息来预测长距离流线。为了提高流线传播预测的准确性，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解码器建模。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纤维束示踪挑战数据集上评估了所提出的方法，有效流线率达到66.2%，白质覆盖率达到63.8%，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，与基于循环神经网络的方法相比，所提出的方法展示了其处理各种扩散磁共振成像采集方案的能力，白质覆盖率提高了5.7%，过度延伸率降低了4.1%。
