Temporal Huber Regularization for DCE-MRI
Temporal Huber Regularization for DCE-MRI
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DOI：
10.1007/s10851-020-00985-2
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发表时间：
2020-09-18
影响因子：
2
通讯作者：
Kolehmainen, Ville
中科院分区：
文献类型：
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作者：
Hanhela, Matti;Kettunen, Mikko;Kolehmainen, Ville
动态对比增强磁共振成像（DCE-MRI）用于研究微血管结构和组织灌注。在DCE-MRI中，将大量钆基造影剂注射到血流中，并从MRI数据的时间序列中估计造影剂流动引起的时空变化。足够的时间分辨率通常只能通过使用对时间序列中的每个图像产生欠采样数据的成像协议来获得。这导致了基于压缩感知的图像重建方法的流行，该方法同时重建时间序列中的所有图像，并通过稀疏性促进正则化函数将图像之间的时间耦合引入问题中。我们提出在DCE-MRI中使用Huber惩罚进行时间正则化，并将其与全变分、全广义变分和基于平滑的时间正则化模型进行比较。我们还研究了空间正则化对重建的影响，并比较了不同欠采样情况下不同时间分辨率下的重建精度。采用模拟和实验的大鼠脑标本径向金角DCE-MRI数据对这些方法进行了测试。结果表明，Huber正则化与基于总变分的模型重建精度相近，但计算时间明显加快。
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to study microvascular structure and tissue perfusion. In DCE-MRI, a bolus of gadolinium-based contrast agent is injected into the blood stream and spatiotemporal changes induced by the contrast agent flow are estimated from a time series of MRI data. Sufficient time resolution can often only be obtained by using an imaging protocol which produces undersampled data for each image in the time series. This has lead to the popularity of compressed sensing-based image reconstruction approaches, where all the images in the time series are reconstructed simultaneously, and temporal coupling between the images is introduced into the problem by a sparsity promoting regularization functional. We propose the use of Huber penalty for temporal regularization in DCE-MRI, and compare it to total variation, total generalized variation and smoothness-based temporal regularization models. We also study the effect of spatial regularization to the reconstruction and compare the reconstruction accuracy with different temporal resolutions due to varying undersampling. The approaches are tested using simulated and experimental radial golden angle DCE-MRI data from a rat brain specimen. The results indicate that Huber regularization produces similar reconstruction accuracy with the total variation-based models, but the computation times are significantly faster.