Galinsky Vitaly L, Frank Lawrence R
IEEE Trans Med Imaging. 2015 May;34(5):1177-93. doi: 10.1109/TMI.2014.2380812. Epub 2014 Dec 18.
We have developed a method for the simultaneous estimation of local diffusion and the global fiber tracts based upon the information entropy flow that computes the maximum entropy trajectories between locations and depends upon the global structure of the multi-dimensional and multi-modal diffusion field. Computation of the entropy spectrum pathways requires only solving a simple eigenvector problem for the probability distribution for which efficient numerical routines exist, and a straight forward integration of the probability conservation through ray tracing of the convective modes guided by a global structure of the entropy spectrum coupled with a small scale local diffusion. The intervoxel diffusion is sampled by multi b-shell multi q-angle diffusion weighted imaging data expanded in spherical waves. This novel approach to fiber tracking incorporates global information about multiple fiber crossings in every individual voxel and ranks it in the most scientifically rigorous way. This method has potential significance for a wide range of applications, including studies of brain connectivity.
我们基于信息熵流开发了一种同时估计局部扩散和全局纤维束的方法,该方法通过计算位置之间的最大熵轨迹来实现,并且依赖于多维多模态扩散场的全局结构。熵谱路径的计算仅需要针对概率分布求解一个简单的特征向量问题,而对于该问题存在高效的数值例程,并且通过由熵谱的全局结构引导的对流模式的光线追踪以及小规模局部扩散来对概率守恒进行直接积分。体素间扩散通过在球面波中展开的多b壳多q角扩散加权成像数据进行采样。这种新颖的纤维追踪方法在每个体素中纳入了有关多个纤维交叉的全局信息,并以最科学严谨的方式对其进行排序。该方法在包括脑连接性研究在内的广泛应用中具有潜在意义。