Harms R L, Fritz F J, Tobisch A, Goebel R, Roebroeck A
Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands; Brain Innovation B.V., Maastricht, The Netherlands.
Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands.
Neuroimage. 2017 Jul 15;155:82-96. doi: 10.1016/j.neuroimage.2017.04.064. Epub 2017 Apr 27.
Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results.
用于扩散磁共振成像（dMRI）的生物物理多室模型取得了进展，因其在将dMRI信号与潜在细胞微观结构相关联方面比扩散张量成像（DTI）具有更高的特异性，故而受到欢迎。已经开发了大量此类扩散微观结构模型，并且每个流行模型都有其自身（通常不同）的优化算法、噪声模型和初始化策略来估计其参数图。由于数据拟合、准确性和精度难以验证，这给扩散微观结构模型结果的可比性和通用性带来了额外挑战。此外，非线性优化在计算上代价高昂，导致运行时间非常长，这在大型群体或总体研究中可能令人望而却步。在本技术说明中，我们研究了几种优化算法和初始化策略在一些最流行的扩散微观结构模型（包括神经突方向离散密度成像（NODDI）和用于磁共振扩散张量成像的紧凑球形方法（CHARMED））上的性能。我们评估是否存在一种性能良好的单一优化方法，可应用于许多模型，并且在运行时间和拟合方面都能达到同等水平。所有模型、算法和策略都在图形处理单元（GPU）上实现，以消除运行时间限制，借此我们能在几秒到几分钟内完成全脑数据集的拟合。然后，我们针对三种常见的优化算法和三种参数初始化策略，评估了不同复杂度的不同模型的拟合度、准确性、精度和运行时间。在两项采用不同采集协议的总体研究中，我们对每组十名受试者的实际数据所实现的拟合质量的可变性进行了评估。我们发现，优化算法和多步优化方法对不同受试者和不同采集协议的性能和稳定性有相当大的影响。结果发现，无梯度的鲍威尔共轭方向算法在运行时间、拟合度、准确性和精度方面优于其他常见算法。参数初始化方法被发现尤其与更复杂的模型相关，比如那些每个体素涉及多个纤维方向的模型。对于这些模型，与单步拟合相比，一种在后期优化步骤中从早期优化步骤的较简单模型初始化或固定参数值的拟合级联方法进一步改善了运行时间、拟合度、准确性和精度。这确立并提供了标准，通过这些标准可以在更短的运行时间内实现稳健的拟合和准确性。这对于在大型群体或总体研究中使用扩散微观结构建模以及将微观结构参数图与纤维束成像结果相结合尤其重要。
