学习空间连续的纤维取向函数
Learning Spatially-Continuous Fiber Orientation Functions
AI总结：
本文提出 FENRI，从低分辨率弥散加权图像学习空间连续的纤维取向密度函数，并构建模拟评估数据集，证明其可提升纤维束成像的流线重建效果。
摘要：
我们对人类连接组的理解从根本上受到弥散磁共振图像分辨率的限制。使用纤维束成像重建连接组的组成神经通路，需要遵循连续的纤维方向场。通常，该场是在低分辨率、含噪的弥散磁共振图像中通过简单的三线性插值得到的。然而，三线性插值难以在低质量数据中追踪细微尺度的变化。近期用于弥散磁共振图像超分辨率的深度学习方法聚焦于上采样到固定空间网格，但这无法满足纤维束成像对连续场的需求。在本工作中，我们提出 FENRI，这是一种从低分辨率弥散加权图像中学习空间连续纤维取向密度函数的新方法。为量化 FENRI 在纤维束成像中的能力，我们还引入了一个扩展的模拟数据集，用于评估深度学习纤维束成像模型。我们证明，FENRI 能够从真实的低质量数据中准确预测高分辨率纤维取向，并且基于 FENRI 的纤维束成像相比当前使用的三线性插值可改善流线重建。
英文摘要：
Our understanding of the human connectome is fundamentally limited by the resolution of diffusion MR images. Reconstructing a connectome's constituent neural pathways with tractography requires following a continuous field of fiber directions. Typically, this field is found with simple trilinear interpolation in low-resolution, noisy diffusion MRIs. However, trilinear interpolation struggles following fine-scale changes in low-quality data. Recent deep learning methods in super-resolving diffusion MRIs have focused on upsampling to a fixed spatial grid, but this does not satisfy tractography's need for a continuous field. In this work, we propose FENRI, a novel method that learns spatially-continuous fiber orientation density functions from low-resolution diffusion-weighted images. To quantify FENRI's capabilities in tractography, we also introduce an expanded simulated dataset built for evaluating deep-learning tractography models. We demonstrate that FENRI accurately predicts high-resolution fiber orientations from realistic low-quality data, and that FENRI-based tractography offers improved streamline reconstruction over the current use of trilinear interpolation.
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。
