CERMAC, San Raffaele Scientific Institute, Milan, Italy.
Neuroimage. 2010 Jan 15;49(2):1446-58. doi: 10.1016/j.neuroimage.2009.09.033. Epub 2009 Sep 23.
Spherical deconvolution methods have been applied to diffusion MRI to improve diffusion tensor tractography results in brain regions with multiple fibre crossing. Recent developments, such as the introduction of non-negative constraints on the solution, allow a more accurate estimation of fibre orientations by reducing instability effects due to noise robustness. Standard convolution methods do not, however, adequately model the effects of partial volume from isotropic tissue, such as gray matter, or cerebrospinal fluid, which may degrade spherical deconvolution results. Here we use a newly developed spherical deconvolution algorithm based on an adaptive regularization (damped version of the Richardson-Lucy algorithm) to reduce isotropic partial volume effects. Results from both simulated and in vivo datasets show that, compared to a standard non-negative constrained algorithm, the damped Richardson-Lucy algorithm reduces spurious fibre orientations and preserves angular resolution of the main fibre orientations. These findings suggest that, in some brain regions, non-negative constraints alone may not be sufficient to reduce spurious fibre orientations. Considering both the speed of processing and the scan time required, this new method has the potential for better characterizing white matter anatomy and the integrity of pathological tissue.
球谐分解方法已被应用于扩散 MRI 中，以改善在存在多纤维交叉的脑区的扩散张量纤维追踪结果。最近的发展，如对解施加非负约束，可以通过降低由于噪声稳健性引起的不稳定性效应，更准确地估计纤维方向。然而，标准卷积方法不能充分地模拟各向同性组织（如灰质或脑脊液）的部分容积效应，这可能会降低球谐分解的结果。在这里，我们使用了一种新开发的基于自适应正则化（Richardson-Lucy 算法的阻尼版本）的球谐分解算法来减少各向同性部分容积效应。来自模拟和体内数据集的结果表明，与标准的非负约束算法相比，阻尼 Richardson-Lucy 算法减少了虚假纤维方向，并保持了主要纤维方向的角分辨率。这些发现表明，在某些脑区，非负约束本身可能不足以减少虚假纤维方向。考虑到处理速度和所需的扫描时间，这种新方法有可能更好地描述白质解剖结构和病变组织的完整性。
