Tremblay Julie, Martínez-Montes Eduardo, Vannasing Phetsamone, Nguyen Dang K, Sawan Mohamad, Lepore Franco, Gallagher Anne
LIONLAB, Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Canada.
Cuban Neuroscience Center, Havana, Cuba.
Biomed Opt Express. 2018 Jun 7;9(7):2994-3016. doi: 10.1364/BOE.9.002994. eCollection 2018 Jul 1.
Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique that elicits growing interest for research and clinical applications. In the last decade, efforts have been made to develop a mathematical framework in order to image the effective sources of hemoglobin variations in brain tissues. Different approaches can be used to impose additional information or constraints when reconstructing the cerebral images of an ill-posed problem. The goal of this study is to compare the performance and limitations of several source localization techniques in the context of fNIRS tomography using individual anatomical magnetic resonance imaging (MRI) to model light propagation. The forward problem is solved using a Monte Carlo simulation of light propagation in the tissues. The inverse problem has been linearized using the Rytov approximation. Then, Tikhonov regularization applied to least squares, truncated singular value decomposition, back-projection, L1-norm regularization, minimum norm estimates, low resolution electromagnetic tomography and Bayesian model averaging techniques are compared using a receiver operating characteristic analysis, blurring and localization error measures. Using realistic simulations (n = 450) and data acquired from a human participant, this study depicts how these source localization techniques behave in a human head fNIRS tomography. When compared to other methods, Bayesian model averaging is proposed as a promising method in DOT and shows great potential to improve specificity, accuracy, as well as to reduce blurring and localization error even in presence of noise and deep sources. Classical reconstruction methods, such as regularized least squares, offer better sensitivity but higher blurring; while more novel L1-based method provides sparse solutions with small blurring and high specificity but lower sensitivity. The application of these methods is also demonstrated experimentally using visual fNIRS experiment with adult participant.
功能近红外光谱技术（fNIRS）是一种非侵入性成像技术，在研究和临床应用中引发了越来越多的关注。在过去十年中，人们致力于开发一个数学框架，以便对脑组织中血红蛋白变化的有效来源进行成像。在重建不适定问题的脑图像时，可以使用不同的方法来施加额外的信息或约束。本研究的目的是在fNIRS断层扫描的背景下，使用个体解剖磁共振成像（MRI）来模拟光传播，比较几种源定位技术的性能和局限性。正向问题通过组织中光传播的蒙特卡罗模拟来解决。逆问题已使用Rytov近似进行线性化。然后，使用接收器操作特征分析、模糊和定位误差测量，比较应用于最小二乘法的Tikhonov正则化、截断奇异值分解、反投影、L1范数正则化、最小范数估计、低分辨率电磁断层扫描和贝叶斯模型平均技术。通过逼真的模拟（n = 450）和从一名人类参与者获取的数据，本研究描绘了这些源定位技术在人体头部fNIRS断层扫描中的表现。与其他方法相比，贝叶斯模型平均被认为是一种在光学断层成像中有前景的方法，即使在存在噪声和深部源的情况下也显示出提高特异性、准确性以及减少模糊和定位误差的巨大潜力。经典的重建方法，如正则化最小二乘法，具有更好的灵敏度但模糊度更高；而更新颖的基于L1的方法提供具有小模糊度和高特异性但灵敏度较低的稀疏解。这些方法的应用也通过对成年参与者进行的视觉fNIRS实验进行了实验验证。
