基于编码-解码循环神经网络的脑白质WMTI-Watson模型参数估计
Parameter estimation for WMTI-Watson model of white matter using encoder-decoder recurrent neural network
摘要：
扩散MRI信号的生物物理建模提供了对特定微观结构组织特性的估计。虽然非线性最小二乘（NLLS）等非线性优化方法是最常用的模型估计方法，但它存在局部极小值和高计算成本的问题。深度学习方法正在稳步取代NL拟合，但有一个限制，即模型需要针对每个采集协议和噪声水平进行重新训练。提出了白质道完整性（WMTI）-Watson模型作为白质扩散的标准模型的实现，其从扩散和峰度张量（DKI）估计模型参数。在这里，我们提出了一种基于编码器-解码器循环神经网络（RNN）的深度学习方法，以提高WMTI-Watson的鲁棒性并加速其参数估计。我们使用嵌入的方法，使模型不敏感的电势差的训练数据和实验数据之间的分布。因此，这种基于RNN的求解器具有计算效率高的优点，并且更容易转换为其他数据集，无论采集协议和底层参数分布如何，只要DKI是从数据中预先计算的。在这项研究中，我们评估了NLLS，基于RNN的方法和多层感知器（MLP）在大鼠和人脑的合成和体内数据集上的性能。我们发现，所提出的基于RNN的拟合方法具有比NLLS大大减少计算时间（从数小时到数秒）的优点，具有相似的准确度和精度，但鲁棒性更高，并且比MLP更好地翻译新数据集。
英文摘要：
Biophysical modelling of the diffusion MRI signal provides estimates of specific microstructural tissue properties. Although nonlinear optimization such as non-linear least squares (NLLS) is the most widespread method for model estimation, it suffers from local minima and high computational cost. Deep Learning approaches are steadily replacing NL fitting, but come with the limitation that the model needs to be retrained for each acquisition protocol and noise level. The White Matter Tract Integrity (WMTI)-Watson model was proposed as an implementation of the Standard Model of diffusion in white matter that estimates model parameters from the diffusion and kurtosis tensors (DKI). Here we proposed a deep learning approach based on the encoder-decoder recurrent neural network (RNN) to increase the robustness and accelerate the parameter estimation of WMTI-Watson. We use an embedding approach to render the model insensitive to potential differences in distributions between training data and experimental data. This RNN-based solver thus has the advantage of being highly efficient in computation and more readily translatable to other datasets, irrespective of acquisition protocol and underlying parameter distributions as long as DKI was pre-computed from the data. In this study, we evaluated the performance of NLLS, the RNN-based method and a multilayer perceptron (MLP) on synthetic and in vivo datasets of rat and human brain. We showed that the proposed RNN-based fitting approach had the advantage of highly reduced computation time over NLLS (from hours to seconds), with similar accuracy and precision but improved robustness, and superior translatability to new datasets over MLP.
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