论文标题
Atlas驱动的深度学习（ADL） - 扩散加权MRI的应用
Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI
论文作者
论文摘要
深度学习具有估计扩散加权磁共振成像（DMRI）中的生物标志物的巨大潜力。另一方面，Atlases是建模生物标志物的时空变异性的独特工具。在本文中，我们提出了第一个框架，以利用DMRI中的生物标志物估计来利用深度学习和地图集。我们的框架依赖于非线性扩散张量注册来计算生物标志物地图集并估算地图集可靠性图。我们还使用非线性张量注册来使地图集与受试者对齐并估计该比对的误差。除DMRI信号外，我们使用生物标志物图集，地图集可靠性图和对齐误差图作为生物标志物估计的深度学习模型的输入。我们使用我们的框架来估计从70名新生儿的测试队列中，从下采样的DMRI数据中估计分数各向异性和神经突导向分散。结果表明，我们的方法明显优于标准估计方法以及最新的深度学习技术。对于更强的测量下采样因子，我们的方法也更强大。我们的研究表明，深度学习和地图集的优势可以协同合并，以实现DMRI数据的生物标志物估计中前所未有的准确性。
Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In this paper, we propose the first framework to exploit both deep learning and atlases for biomarker estimation in dMRI. Our framework relies on non-linear diffusion tensor registration to compute biomarker atlases and to estimate atlas reliability maps. We also use nonlinear tensor registration to align the atlas to a subject and to estimate the error of this alignment. We use the biomarker atlas, atlas reliability map, and alignment error map, in addition to the dMRI signal, as inputs to a deep learning model for biomarker estimation. We use our framework to estimate fractional anisotropy and neurite orientation dispersion from down-sampled dMRI data on a test cohort of 70 newborn subjects. Results show that our method significantly outperforms standard estimation methods as well as recent deep learning techniques. Our method is also more robust to stronger measurement down-sampling factors. Our study shows that the advantages of deep learning and atlases can be synergistically combined to achieve unprecedented accuracy in biomarker estimation from dMRI data.
