Machine-learning-informed parameter estimation improves the reliability of spinal cord diffusion MRI
Machine-learning-informed parameter estimation improves the reliability of spinal cord diffusion MRI
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发表时间：
2023-01
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通讯作者：
Ting Gong;Francesco Grussu;C. Wheeler-Kingshott;D. Alexander;Hui Zhang
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作者：
Ting Gong;Francesco Grussu;C. Wheeler-Kingshott;D. Alexander;Hui Zhang
目的：我们解决的挑战，不准确的参数估计时，扩散MRI的信噪比（SNR）是非常低的，在脊髓。传统的最大似然估计（MLE）的精度高度依赖于初始化。不利的选择可能导致次优的参数估计。目前解决这个问题的方法，如网格搜索（GS），可以大大增加计算时间。方法：我们提出了一种机器学习（ML）知情的MLE方法，将传统的MLE与ML方法协同结合。近年来，为了提高参数估计的速度和精度，基于ML的方法得到了发展。然而，当信噪比较低时，它们会在估计参数中产生较高的系统偏差。在所提出的ML-MLE方法中，训练人工神经网络模型以有效地为MLE提供合理的初始化，最终解决方案由MLE确定，避免通常影响纯ML估计的偏差。结果如下：以神经突取向分散和密度成像的参数估计为例，仿真和体内实验表明，ML-MLE方法可以减少受CSF污染影响的白色体素中常规MLE的离群值估计。与GS-MLE相比，它还加快了计算速度。结论：与GS-MLE相比，ML-MLE方法可以提高参数估计的可靠性，减少计算时间，使其成为低信噪比扩散数据集的实用工具。
Purpose : We address the challenge of inaccurate parameter estimation in diffusion MRI when the signal-to-noise ratio (SNR) is very low, as in the spinal cord. The accuracy of conventional maximum-likelihood estimation (MLE) depends highly on initialisation. Unfavourable choices could result in suboptimal parameter estimates. Current methods to address this issue, such as grid search (GS) can increase computation time substantially. Methods : We propose a machine learning (ML) informed MLE approach that combines conventional MLE with ML approaches synergistically. ML-based methods have been developed recently to improve the speed and precision of parameter estimation. However, they can generate high systematic bias in estimated parameters when SNR is low. In the proposed ML-MLE approach, an artificial neural network model is trained to provide sensible initialisation for MLE efficiently, with the final solution determined by MLE, avoiding biases typically affecting pure ML estimations. Results : Using parameter estimation of neurite orientation dispersion and density imaging as an example, simulation and in vivo experiments suggest that the ML-MLE method can reduce outlier estimates from conventional MLE in white matter voxels affected by CSF contamination. It also accelerates computation compared to GS-MLE. Conclusion : The ML-MLE method can improve the reliability of parameter estimation with reduced computation time compared to GS-MLE, making it a practical tool for diffusion dataset with low SNR.