Pontabry Julien, Rousseau François
LSIIT, UMR 7005 CNRS-Université de Strasbourg.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):209-16. doi: 10.1007/978-3-642-23629-7_26.
By assuming that orientation information of brain white matter fibers can be inferred from Diffusion Weighted Magnetic Resonance Imaging (DWMRI) measurements, tractography algorithms provide an estimation of the brain connectivity in-vivo. The two key ingredients of tractography are the diffusion model (tensor, high-order tensor, Q-ball, etc.) and the way to deal with uncertainty during the tracking process (deterministic vs probabilistic). In this paper, we investigate the use of an analytical Q-ball model for the diffusion data within a well-formalized particle filtering framework. The proposed method is validated and compared to other tracking algorithms on the MICCAI'09 contest Fiber Cup phantom and on in-vivo brain DWMRI data.
通过假设脑白质纤维的方向信息可从扩散加权磁共振成像（DWMRI）测量中推断出来，纤维束成像算法可对活体脑连接性进行估计。纤维束成像的两个关键要素是扩散模型（张量、高阶张量、Q球等）以及在追踪过程中处理不确定性的方式（确定性与概率性）。在本文中，我们研究在一个形式完备的粒子滤波框架内，将解析Q球模型用于扩散数据。所提出的方法在MICCAI'09竞赛的纤维杯模型以及活体脑DWMRI数据上进行了验证，并与其他追踪算法进行了比较。
