利用机器学习实现胎儿大脑在体流线纤维追踪
Streamline tractography of the fetal brain in utero with machine learning
AI总结：
针对胎儿大脑dMRI信号质量低和发育不成熟导致纤维追踪困难的问题，提出首个融合多源信息与神经网络的胎儿大脑纤维追踪机器学习模型，在11例测试中表现优异，显著提升了在体研究大脑发育的能力。
摘要：
扩散加权磁共振成像（dMRI）是研究大脑白质纤维束和结构连接性的唯一非侵入性工具。这些评估严重依赖于纤维追踪技术，该技术重建代表白质纤维的虚拟流线。大量精力被投入到改进成年人大脑的纤维追踪方法上，而胎儿大脑的纤维追踪却在很大程度上被忽视了。由于dMRI信号质量低、大脑结构未成熟且快速发育，以及参考数据匮乏，胎儿纤维追踪面临独特的困难。本工作提出了首个用于胎儿纤维追踪的机器学习模型。模型输入包含五种信息源：（1）由拟合dMRI信号的扩散张量推断出的纤维方向；（2）近期传播步骤的方向；（3）全局空间信息，编码为到大脑皮层关键点的距离；（4）组织分割信息；（5）通过图谱提供的关于预期局部纤维方向的先验信息。为了缓解局部张量估计误差，使用卷积和注意力神经网络模块对扩散张量图像中当前点周围的大空间上下文进行编码。此外，假设的下一个点的扩散张量信息也包含在模型输入中。应用基于解剖学约束纤维追踪的过滤规则来修剪不合理的流线。我们在手动精炼的全脑胎儿纤维束图上训练了该模型，并在11次测试扫描的独立集上验证了训练好的模型，胎龄在23至36周之间。结果表明，我们提出的方法在所有评估的纤维束上均实现了优越的性能。新方法可以显著提升dMRI在研究子宫内正常和异常大脑发育方面的能力。
英文摘要：
Diffusion-weighted magnetic resonance imaging (dMRI) is the only non-invasive tool for studying white matter tracts and structural connectivity of the brain. These assessments rely heavily on tractography techniques, which reconstruct virtual streamlines representing white matter fibers. Much effort has been devoted to improving tractography methodology for adult brains, while tractography of the fetal brain has been largely neglected. Fetal tractography faces unique difficulties due to low dMRI signal quality, immature and rapidly developing brain structures, and paucity of reference data. This work presents the first machine learning model for fetal tractography. The model input consists of five sources of information: (1) Fiber orientation, inferred from a diffusion tensor fit to the dMRI signal; (2) Directions of recent propagation steps; (3) Global spatial information, encoded as distances to keypoints in the brain cortex; (4) Tissue segmentation information; and (5) Prior information about the expected local fiber orientations supplied with an atlas. In order to mitigate the local tensor estimation error, a large spatial context around the current point in the diffusion tensor image is encoded using convolutional and attention neural network modules. Moreover, the diffusion tensor information at a hypothetical next point is included in the model input. Filtering rules based on anatomically constrained tractography are applied to prune implausible streamlines. We trained the model on manually-refined whole-brain fetal tractograms and validated the trained model on an independent set of 11 test scans with gestational ages between 23 and 36 weeks. Results show that our proposed method achieves superior performance across all evaluated tracts. The new method can significantly advance the capabilities of dMRI for studying normal and abnormal brain development in utero.
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。
