Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter.
Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter.
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DOI：
10.1016/j.neuroimage.2021.118601
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发表时间：
2021-12-01
期刊：
影响因子：
5.7
通讯作者：
Szczepankiewicz, Filip
中科院分区：
文献类型：
--
作者：
Martins, Joao P. de Almeida;Nilsson, Markus;Lampinen, Bjorn;Palombo, Marco;While, Peter T.;Westin, Carl-Fredrik;Szczepankiewicz, Filip
白色物质微观结构的具体特征可以通过使用生物物理模型来解释弛豫扩散MRI脑数据来研究。虽然更复杂的模型有可能揭示组织的更多细节，但它们也会导致耗时的参数估计，由于退化拟合景观中局部最小值的普遍存在，这些参数估计可能会收敛到不准确的解决方案。机器学习拟合算法已被提出来加速参数估计，并增加所获得的估计的鲁棒性。到目前为止，基于学习的拟合方法已被限制到微观结构模型与独立的模型参数的数量减少，密集的训练数据集很容易生成。此外，机器学习可以在多大程度上缓解退化问题还知之甚少。对于传统的最小二乘求解器，它已被证明，退化可以避免收购与优化的弛豫扩散相关协议，包括张量值扩散编码。机器学习技术是否能够抵消这些收购要求仍有待测试。在这项工作中，我们采用人工神经网络大大加快最近推出的白色物质微观结构的弛豫扩散模型的参数估计。我们还制定了评估功能拟合网络的准确性和灵敏度的策略，并使用这些策略来探索收购协议的影响。开发的学习为基础的拟合管道进行了测试的最佳和次最佳的采集协议获得的弛豫扩散数据。观察到用优化协议训练的网络在短的计算时间内提供准确的参数估计。比较神经网络和最小二乘求解器，我们发现前者的性能受次优协议的影响较小;然而，模型拟合网络仍然容易受到退化问题的影响，并且它们的使用不能完全取代采集协议的精心设计。
Specific features of white matter microstructure can be investigated by using biophysical models to interpret relaxation-diffusion MRI brain data. Although more intricate models have the potential to reveal more details of the tissue, they also incur time-consuming parameter estimation that may converge to inaccurate solutions due to a prevalence of local minima in a degenerate fitting landscape. Machine-learning fitting algorithms have been proposed to accelerate the parameter estimation and increase the robustness of the attained estimates. So far, learning-based fitting approaches have been restricted to microstructural models with a reduced number of independent model parameters where dense sets of training data are easy to generate. Moreover, the degree to which machine learning can alleviate the degeneracy problem is poorly understood. For conventional least-squares solvers, it has been shown that degeneracy can be avoided by acquisition with optimized relaxation-diffusion-correlation protocols that include tensor-valued diffusion encoding. Whether machine-learning techniques can offset these acquisition requirements remains to be tested. In this work, we employ artificial neural networks to vastly accelerate the parameter estimation for a recently introduced relaxation-diffusion model of white matter microstructure. We also develop strategies for assessing the accuracy and sensitivity of function fitting networks and use those strategies to explore the impact of the acquisition protocol. The developed learning-based fitting pipelines were tested on relaxation-diffusion data acquired with optimal and sub-optimal acquisition protocols. Networks trained with an optimized protocol were observed to provide accurate parameter estimates within short computational times. Comparing neural networks and least-squares solvers, we found the performance of the former to be less affected by sub-optimal protocols; however, model fitting networks were still susceptible to degeneracy issues and their use could not fully replace a careful design of the acquisition protocol.
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影响因子：
4.8
作者：
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