School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia.
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China.
Med Image Anal. 2022 Jul;79:102431. doi: 10.1016/j.media.2022.102431. Epub 2022 Apr 6.
Mapping the human connectome using fiber-tracking permits the study of brain connectivity and yields new insights into neuroscience. However, reliable connectome reconstruction using diffusion magnetic resonance imaging (dMRI) data acquired by widely available clinical protocols remains challenging, thus limiting the connectome/tractography clinical applications. Here we develop fiber orientation distribution (FOD) network (FOD-Net), a deep-learning-based framework for FOD angular super-resolution. Our method enhances the angular resolution of FOD images computed from common clinical-quality dMRI data, to obtain FODs with quality comparable to those produced from advanced research scanners. Super-resolved FOD images enable superior tractography and structural connectome reconstruction from clinical protocols. The method was trained and tested with high-quality data from the Human Connectome Project (HCP) and further validated with a local clinical 3.0T scanner as well as with another public available multicenter-multiscanner dataset. Using this method, we improve the angular resolution of FOD images acquired with typical single-shell low-angular-resolution dMRI data (e.g., 32 directions, b=1000s/mm) to approximate the quality of FODs derived from time-consuming, multi-shell high-angular-resolution dMRI research protocols. We also demonstrate tractography improvement, removing spurious connections and bridging missing connections. We further demonstrate that connectomes reconstructed by super-resolved FODs achieve comparable results to those obtained with more advanced dMRI acquisition protocols, on both HCP and clinical 3.0T data. Advances in deep-learning approaches used in FOD-Net facilitate the generation of high quality tractography/connectome analysis from existing clinical MRI environments. Our code is freely available at https://github.com/ruizengalways/FOD-Net.
利用纤维追踪绘制人类连接组图谱可以研究大脑连接，并为神经科学提供新的见解。然而，使用广泛可用的临床协议获取的扩散磁共振成像 (dMRI) 数据可靠地重建连接组/轨迹仍然具有挑战性，从而限制了连接组/轨迹的临床应用。在这里，我们开发了纤维方向分布 (FOD) 网络 (FOD-Net)，这是一种基于深度学习的 FOD 角超分辨率框架。我们的方法提高了从常见临床质量 dMRI 数据计算得出的 FOD 的角分辨率，从而获得与从先进研究扫描仪生成的质量相当的 FOD。超分辨率 FOD 图像能够从临床协议中进行更好的轨迹追踪和结构连接组重建。该方法使用来自人类连接组计划 (HCP) 的高质量数据进行训练和测试，并进一步使用本地临床 3.0T 扫描仪以及另一个公共可用的多中心多扫描仪数据集进行验证。使用该方法，我们提高了典型单壳低角分辨率 dMRI 数据（例如，32 个方向，b=1000s/mm）获取的 FOD 图像的角分辨率，使其接近来自耗时、多壳高角分辨率 dMRI 研究协议的 FOD 的质量。我们还证明了轨迹追踪的改进，消除了虚假连接并桥接了缺失的连接。我们进一步证明，通过超分辨率 FOD 重建的连接组与使用更先进的 dMRI 采集协议获得的连接组具有相当的结果，无论是在 HCP 还是临床 3.0T 数据上。FOD-Net 中使用的深度学习方法的进步促进了从现有临床 MRI 环境中生成高质量的轨迹追踪/连接组分析。我们的代码可在 https://github.com/ruizengalways/FOD-Net 上免费获得。
