Neural Networks for parameter estimation in microstructural MRI: a study with a high-dimensional diffusion-relaxation model of white matter microstructure
Neural Networks for parameter estimation in microstructural MRI: a study with a high-dimensional diffusion-relaxation model of white matter microstructure
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DOI：
10.1101/2021.03.12.435163
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发表时间：
2021-03
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影响因子：
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通讯作者：
João P. de Almeida Martins;M. Nilsson;Björn Lampinen;M. Palombo;P. T. While;C. Westin;F. Szczepankiewicz
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文献类型：
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作者：
João P. de Almeida Martins;M. Nilsson;Björn Lampinen;M. Palombo;P. T. While;C. Westin;F. Szczepankiewicz
白质微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导致不准确的解决方案。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仅限于低维微观结构模型，其中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解算器，已经证明，可以通过使用包括张量值扩散编码的优化弛豫扩散相关协议进行采集来避免退化;机器学习技术是否可以抵消这些采集要求仍有待测试。在这项工作中，我们采用深度神经网络来大大加速最近引入的组织微观结构的高维弛豫扩散模型的拟合。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采集协议设计对网络性能的影响。所开发的基于学习的拟合管道进行了测试的弛豫扩散数据与优化和子采样采集协议。我们没有发现任何证据表明机器学习算法本身可以取代精心设计的采集协议或纠正退化的拟合景观。
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 con-verge 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 lower-dimensional microstructural models 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 require-ments remains to be tested. In this work, we employ deep neural networks to vastly accelerate the fitting of a recently introduced high-dimensional relaxation-diffusion model of tissue microstructure. We also develop strategies for assessing the accuracy and sensitivity of function fitting networks and use those strategies to explore the impact of acquisition protocol design on the performance of the network. The developed learning-based fitting pipelines were tested on relaxation-diffusion data acquired with optimized and sub-sampled acquisition protocols. We found no evidence that machine-learning algorithms can by themselves replace a careful design of the acquisition protocol or correct for a degenerate fitting landscape.