论文标题
使用基于补丁的深层图像先验的主体特定的定量敏感性映射
Subject-specific quantitative susceptibility mapping using patch based deep image priors
论文作者
论文摘要
定量敏感性映射是一种参数成像技术，可从MRI相测量中估算生物组织的磁敏感性。估计易感图图的这个问题不明显。利用信号特性（如光滑度和稀疏性）的正规恢复方法可改善重建，但会遭受过度光滑的伪像。深度学习方法表现出巨大的潜力，并产生了伪像减少的地图。但是，为了合理的重建和网络概括，它们需要大量的培训数据集，从而增加数据获取时间。为了克服这个问题，我们提出了一个特定于主题的，基于贴片的，无监督的学习算法，以估计易感性图。我们通过使用深层卷积神经网络利用在地图斑块上的冗余来充分解决问题。我们将易感性图的恢复为正规化优化问题，并采用了交替的最小化策略来解决它。我们在3D Invivo数据集上测试了该算法，并且在定性和定量上，证明了对竞争方法的重建改进了。
Quantitative Susceptibility Mapping is a parametric imaging technique to estimate the magnetic susceptibilities of biological tissues from MRI phase measurements. This problem of estimating the susceptibility map is ill posed. Regularized recovery approaches exploiting signal properties such as smoothness and sparsity improve reconstructions, but suffer from over-smoothing artifacts. Deep learning approaches have shown great potential and generate maps with reduced artifacts. However, for reasonable reconstructions and network generalization, they require numerous training datasets resulting in increased data acquisition time. To overcome this issue, we proposed a subject-specific, patch-based, unsupervised learning algorithm to estimate the susceptibility map. We make the problem well-posed by exploiting the redundancies across the patches of the map using a deep convolutional neural network. We formulated the recovery of the susceptibility map as a regularized optimization problem and adopted an alternating minimization strategy to solve it. We tested the algorithm on a 3D invivo dataset and, qualitatively and quantitatively, demonstrated improved reconstructions over competing methods.
