Department of Mathematics, University of Guanajuato, Mineral de Valenciana Guanajuato, Mexico.
Med Phys. 2011 Sep;38(9):5239-53. doi: 10.1118/1.3626571.
Diffusion tensor magnetic resonance imaging is widely used to study the structure of the fiber pathways of brain white matter. However, the diffusion tensor cannot capture complex intravoxel fiber architecture such as fiber crossings of bifurcations. Consequently, a number of methods have been proposed to recover intravoxel fiber bundle orientations from high angular resolution diffusion imaging scans, optimized to resolve fiber crossings. It is important to improve the brain tractography by applying these multifiber methods to diffusion tensor protocols with a clinical b- value (low), which are optimized on computing tensor scalar statistics. In order to characterize the variance among different methods, consequently to be able to select the most appropriate one for a particular application, it is desirable to compare them under identical experimental conditions.
In this work, the authors study how QBall, spherical deconvolution, persistent angular structure, stick and ball, diffusion basis functions, and analytical QBall methods perform under clinically-realistic scanning conditions, where the b-value is typically lower (around 1000 s∕mm(2)), and the number of diffusion encoding orientations is fewer (30-60) than in dedicated high angular resolution diffusion imaging scans. To characterize the performance of the methods, they consider the accuracy of the estimated number of fibers, the relative contribution of each fiber population to the total magnetic resonance signal, and the recovered orientation error for each fiber bundle. To this aim, they use four different sources of data: synthetic data from Gaussian mixture model, cylinder restricted model, and in vivo data from two different acquisition schemes.
Results of their experiments indicate that: (a) it is feasible to apply only a subset of these methods to clinical data sets and (b) it allows one to characterize the performance of each method. In particular, two methods are not feasible to the kind of magnetic resonance diffusion data they test. By the characterization of their systematic behavior, among other conclusions, they report the method which better performs for the estimation of the number of diffusion peaks per voxel, also the method which better estimates the diffusion orientation.
The framework they propose for comparison allows one to effectively characterize and compare the performance of the most frequently used multifiber algorithms under realistic medical settings and realistic signal-to-noise ratio environments. The framework is based on several crossings with a non-orientational bias and different signal models. The results they present are relevant for medical doctors and researchers, interested in the use of the multifiber solution for tractography.
弥散张量磁共振成像被广泛用于研究脑白质纤维束的结构。然而，弥散张量不能捕获复杂的体素内纤维结构，例如分叉的纤维交叉。因此，已经提出了许多方法来从高角分辨率弥散成像扫描中恢复体素内纤维束方向，这些方法经过优化可解决纤维交叉问题。将这些多纤维方法应用于临床 b 值（低值）的弥散张量方案中，以提高脑束追踪的效果非常重要，这些方案是基于张量标量统计数据进行优化的。为了描述不同方法之间的差异，以便能够为特定应用选择最合适的方法，最好在相同的实验条件下对它们进行比较。
在这项工作中，作者研究了 QBall、球形解卷积、持久角结构、棒和球、弥散基函数以及解析 QBall 方法在临床实际扫描条件下的表现，其中 b 值通常较低（约 1000 s∕mm²），并且弥散编码方向的数量比专门的高角分辨率弥散成像扫描少（30-60）。为了描述这些方法的性能，他们考虑了估计纤维数量的准确性、每个纤维群体对总磁共振信号的相对贡献以及每个纤维束的恢复方向误差。为此，他们使用了四种不同的数据来源：来自高斯混合模型、圆柱限制模型以及来自两种不同采集方案的体内数据的合成数据。
他们的实验结果表明：（a）可以将这些方法中的一部分应用于临床数据集，（b）这可以用来描述每种方法的性能。特别是，对于他们测试的磁共振扩散数据，有两种方法是不可行的。通过对其系统行为的描述，以及其他结论，他们报告了一种方法可以更好地估计每个体素的扩散峰数量，以及一种方法可以更好地估计扩散方向。
他们提出的比较框架可以有效地描述和比较最常用的多纤维算法在实际医疗环境和实际信噪比环境下的性能。该框架基于几个具有非定向偏差和不同信号模型的交叉点。他们提出的结果对于对多纤维解决方案用于束追踪感兴趣的医生和研究人员是相关的。
