Wu Haiyong, Chen Geng, Jin Yan, Shen Dinggang, Yap Pew-Thian
School of Information Engineering, Xiaozhuang University, Nanjing, China; Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA.
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina , Chapel Hill, NC , USA.
Front Neuroinform. 2016 Jul 13;10:25. doi: 10.3389/fninf.2016.00025. eCollection 2016.
Global tractography estimates brain connectivity by organizing signal-generating fiber segments in an optimal configuration that best describes the measured diffusion-weighted data, promising better stability than local greedy methods with respect to imaging noise. However, global tractography is computationally very demanding and requires computation times that are often prohibitive for clinical applications. We present here a reformulation of the global tractography algorithm for fast parallel implementation amendable to acceleration using multi-core CPUs and general-purpose GPUs. Our method is motivated by the key observation that each fiber segment is affected by a limited spatial neighborhood. In other words, a fiber segment is influenced only by the fiber segments that are (or can potentially be) connected to its two ends and also by the diffusion-weighted signal in its proximity. This observation makes it possible to parallelize the Markov chain Monte Carlo (MCMC) algorithm used in the global tractography algorithm so that concurrent updating of independent fiber segments can be carried out. Experiments show that the proposed algorithm can significantly speed up global tractography, while at the same time maintain or even improve tractography performance.
全局纤维束成像通过将产生信号的纤维段组织成一种最佳配置来估计脑连接性，这种配置能最好地描述所测量的扩散加权数据，相对于局部贪婪方法而言，有望在成像噪声方面具有更好的稳定性。然而，全局纤维束成像在计算上要求很高，其所需的计算时间对于临床应用来说往往令人望而却步。我们在此提出一种全局纤维束成像算法的重新表述，以便能快速并行实现，适合使用多核CPU和通用GPU进行加速。我们的方法基于一个关键观察结果，即每个纤维段仅受有限空间邻域的影响。换句话说，一个纤维段仅受与其两端相连（或可能相连）的纤维段以及其附近的扩散加权信号的影响。这一观察结果使得全局纤维束成像算法中使用的马尔可夫链蒙特卡罗（MCMC）算法能够并行化，从而可以对独立的纤维段进行并发更新。实验表明，所提出的算法能够显著加快全局纤维束成像速度，同时保持甚至提高纤维束成像性能。
