GenTract：生成式全局纤维束追踪
GenTract: Generative Global Tractography
把纤维束追踪从逐步跟踪或全局优化重构为从 dMRI 到完整流线的条件生成问题，并用扩散模型与流匹配两种范式做对比；对视觉研究者而言，这是一个把生成模型用于结构化曲线/轨迹输出的可复用范式，可迁移到血管、道路等线状结构重建任务。
原文依据 we introduce GenTract, the first generative model for global tractography
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论纤维束追踪是指从扩散磁共振成像（dMRI）中推断大脑白质通路轨迹的过程。局部纤维束追踪方法通过在图像中逐步跟随局部纤维方向估计来构建流线，容易产生误差累积和较高的假阳性率，在噪声较大或分辨率较低的数据上尤为明显。相比之下，全局方法试图优化一组流线，使其与底层纤维方向估计的兼容性最大化，但计算代价高昂。为应对这些挑战，我们提出 GenTract，这是首个用于全局纤维束追踪的生成模型。我们将纤维束追踪构建为一个生成任务，学习从 dMRI 到完整、解剖上合理的流线的直接映射。我们比较了基于扩散的范式与流匹配范式，并将 GenTract 的性能与最优基线进行评测。值得注意的是，GenTract 的精确率分别比次优方法 DDTracking 和 TractOracle 高出 1.8 倍和 2.1 倍。这一优势在具有挑战性的低分辨率和噪声场景下更为显著，此时它比最接近的竞争者高出 3.5 倍。通过在研究级数据上生成高精确率的纤维束图，同时在并不完美的低分辨率数据上保持可靠性，GenTract 为全局纤维束追踪提供了一种有前景的解决方案。
英文原文摘要 1426 字符
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.