Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands.
Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands.
Neuroimage. 2024 Jan;285:120496. doi: 10.1016/j.neuroimage.2023.120496. Epub 2023 Dec 13.
Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.
扩散磁共振成像（dMRI）可用于非侵入式地研究脑组织的微观结构。通过对 dMRI 信号进行拟合，可以从数据中得出各种定量指标，如分数各向异性、神经丝密度和轴突半径图。我们研究了 Fisher 信息矩阵（FIM）和不确定性传播，作为一种通用的方法来量化线性和非线性扩散 MRI 模型中的参数不确定性。与马尔可夫链蒙特卡罗（MCMC）采样相比，FIM 在计算成本低得多的情况下产生了类似的不确定性估计。然后，我们使用获得的数据和模拟数据，列出了几个影响参数方差的特征，包括数据复杂性和信噪比。为了实际应用，我们研究了通过不确定性加权组估计来减少组统计中组内方差的不确定性估计的可能性。这对于检测和抑制成像伪影具有潜在的应用价值。
