Wu Ye, Feng Yuanjing, Shen Dinggang, Yap Pew-Thian
Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China.
Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2018 Sep;11072:45-52. doi: 10.1007/978-3-030-00931-1_6. Epub 2018 Sep 13.
In connectomics, tractography involves tracing connections across gray-white matter boundaries in gyral blades of complex cortical convolutions. To date, most tractography algorithms exhibit gyral bias with fiber streamlines preferentially terminating at gyral crowns rather than sulcal banks or fundi. In this work, we will demonstrate that a multi-tissue global estimation framework of the asymmetric fiber orientation distribution function (AFODF) will mitigate the effects of gyral bias and will allow fiber streamlines at gyral blades to make sharper turns into the cortical gray matter. This is validated using in-vivo data from the Human Connectome Project (HCP), showing that, in a typical gyral blade with high curvature, the fiber streamlines estimated using AFODFs bend more naturally into the cortex than FODFs. Furthermore, we show that AFODF tractography results in better cortico-cortical connectivity.
在连接组学中,纤维束成像涉及追踪复杂皮质褶皱脑回叶片中灰质与白质边界之间的连接。迄今为止,大多数纤维束成像算法都表现出脑回偏向性,纤维流线优先终止于脑回顶部,而非脑沟壁或脑沟底部。在这项研究中,我们将证明非对称纤维取向分布函数(AFODF)的多组织全局估计框架将减轻脑回偏向性的影响,并使脑回叶片处的纤维流线能够更急剧地转向皮质灰质。这通过使用人类连接组计划(HCP)的活体数据得到验证,结果表明,在具有高曲率的典型脑回叶片中,使用AFODF估计的纤维流线比使用FODF估计的纤维流线更自然地弯曲进入皮质。此外,我们表明AFODF纤维束成像能够实现更好的皮质-皮质连接。