论文标题
将定向微观结构模型拟合到使用自我监督的机器学习的扩散分布数据的MRI数据
Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning
论文作者
论文摘要
机器学习是将微结构模型拟合到扩散MRI数据的强大方法。早期的机器学习微观结构成像实现训练了回归器，以使用已知地面真相的合成训练数据以有监督的方式估算模型参数。但是，这种方法的缺点是训练数据的选择会影响拟合的参数值。在这种情况下，自我监督的学习正在成为监督学习的一种有吸引力的替代方法。到目前为止，与同性恋模型（例如静脉内外运动（IVIM））相比，与各向异性结构的方向性相比，也已将监督和自我监督学习的学习应用于各向同性模型，例如玻璃蛋白不连贯运动（IVIM）。在本文中，我们展示了定向微观结构模型的自我监督的机器学习模型。特别是，我们将组合的T1-ball-stick模型拟合到多维扩散（MUDI）挑战扩散 - 删除数据集。与标准的非线性最小二乘拟合相比，我们的自我监督方法显示了模拟和体内大脑数据的参数估计和计算时间的明显改善。为本研究构建的人工神经网的代码可从以下GitHub存储库中公开使用：https：//github.com/jplte/deep-t1-ball-stick
Machine learning is a powerful approach for fitting microstructural models to diffusion MRI data. Early machine learning microstructure imaging implementations trained regressors to estimate model parameters in a supervised way, using synthetic training data with known ground truth. However, a drawback of this approach is that the choice of training data impacts fitted parameter values. Self-supervised learning is emerging as an attractive alternative to supervised learning in this context. Thus far, both supervised and self-supervised learning have typically been applied to isotropic models, such as intravoxel incoherent motion (IVIM), as opposed to models where the directionality of anisotropic structures is also estimated. In this paper, we demonstrate self-supervised machine learning model fitting for a directional microstructural model. In particular, we fit a combined T1-ball-stick model to the multidimensional diffusion (MUDI) challenge diffusion-relaxation dataset. Our self-supervised approach shows clear improvements in parameter estimation and computational time, for both simulated and in-vivo brain data, compared to standard non-linear least squares fitting. Code for the artificial neural net constructed for this study is available for public use from the following GitHub repository: https://github.com/jplte/deep-T1-ball-stick
