Yoon Jongyeon, Rao Mingxing, McMaster Elyssa M, Cho Chloe, Newlin Nancy R, Schilling Kurt G, Landman Bennett A, Moyer Daniel
Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Proc IEEE Int Symp Biomed Imaging. 2025;2025. doi: 10.1109/isbi60581.2025.10981144. Epub 2025 May 12.
Diffusion MRI (dMRI) streamline tractography has been the gold standard for non-invasive estimation of white matter (WM) pathways in the human brain. Recent advancements in deep learning have enabled the generation of streamlines from T1-weighted (T1w) MRI, a more common imaging method. The accuracy of current T1w tracking methods is limited by their recurrent architecture. In the present work, we modify a current state-of-the-art T1w tractography method (CoRNN), replacing recurrent units and its sequential representation with Transformer modules, and modifying both the representation and the prediction network for the fiber orientation distributions. We demonstrate that these changes provide substantial performance benefits over the baseline method, producing high angular consistency with the gold standard dMRI tractogram in healthy normal adult humans.
扩散磁共振成像（dMRI）流线追踪术一直是无创估计人脑白质（WM）通路的金标准。深度学习的最新进展使得能够从T1加权（T1w）MRI（一种更常见的成像方法）生成流线。当前T1w追踪方法的准确性受到其循环架构的限制。在本研究中，我们修改了当前最先进的T1w追踪方法（CoRNN），用Transformer模块替换循环单元及其顺序表示，并修改了纤维方向分布的表示和预测网络。我们证明，这些改变比基线方法具有显著的性能优势，在健康正常成年人中与金标准dMRI纤维束成像具有高度的角度一致性。
