Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information

• URL: http://arxiv.org/abs/2503.03329v1
• Date: Wed, 05 Mar 2025 10:02:35 GMT
• Title: Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information
• Authors: Yiqiong Yang, Yitian Yuan, Baoxing Ren, Ye Wu, Yanqiu Feng, Xinyuan Zhang,
• Abstract summary: Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain.<n>It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders.<n>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.
• Score: 6.669798532124614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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