GenTract：生成式全局束追踪
GenTract: Generative Global Tractography
AI总结：
本文提出GenTract，一种基于生成模型的全局束追踪方法，通过学习从dMRI到完整解剖学合理束流的直接映射，提高了在低分辨率和噪声数据下的精度和可靠性。
摘要：
束追踪是通过扩散磁共振成像（dMRI）推断大脑白质路径轨迹的过程。局部束追踪方法通过逐步跟随局部纤维方向估计来构建束流，易产生误差累积和高假阳性率，尤其是在噪声或低分辨率数据中。相比之下，全局方法试图通过优化束流集合以最大化与底层纤维方向估计的兼容性，但计算成本较高。为解决这些挑战，我们引入GenTract，这是首个生成式全局束追踪模型。我们将束追踪视为生成任务，学习从dMRI到完整、解剖学合理束流的直接映射。我们比较了基于扩散和流匹配的两种范式，并评估了GenTract在与现有最先进基线方法的性能。值得注意的是，GenTract在精度上比次优方法DDTracking和TractOracle分别高出1.8倍和2.1倍。在具有挑战性的低分辨率和噪声设置中，其优势更加明显，比最接近的竞争对手高出3.5倍。通过在研究级数据上产生高精度的束流图，同时在不完美的低分辨率数据上保持可靠性，GenTract代表了全局束追踪的一个有前景的解决方案。
英文摘要：
Tractography is the process of inferring the trajectories of white-matter pathways in the brain from diffusion magnetic resonance imaging (dMRI). Local tractography methods, which construct streamlines by following local fiber orientation estimates stepwise through an image, are prone to error accumulation and high false positive rates, particularly on noisy or low-resolution data. In contrast, global methods, which attempt to optimize a collection of streamlines to maximize compatibility with underlying fiber orientation estimates, are computationally expensive. To address these challenges, we introduce GenTract, the first generative model for global tractography. We frame tractography as a generative task, learning a direct mapping from dMRI to complete, anatomically plausible streamlines. We compare both diffusion-based and flow matching paradigms and evaluate GenTract's performance against state-of-the-art baselines. Notably, GenTract achieves precision 1.8x and 2.1x higher than the next-best methods, DDTracking and TractOracle, respectively. This advantage becomes even more pronounced in challenging low-resolution and noisy settings, where it outperforms the closest competitor by a factor of 3.5. By producing tractograms with high precision on research-grade data while also maintaining reliability on imperfect, lower-resolution data, GenTract represents a promising solution for global tractography.
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。
