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.

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g基造影剂推注到血流中，并根据MRI数据的时间序列估算由造影剂流动引起的时空变化。通常只能通过使用成像协议来获得足够的时间分辨率，该协议会为时间序列中的每个图像生成欠采样数据。这导致基于压缩感测的图像重建方法的普及，其中同时重建时间序列中的所有图像，并且通过稀疏性促进正则化功能将图像之间的时间耦合引入到问题中。我们建议在DCE-MRI中将Huber罚分用于时间正则化，并将其与总变异，总广义变异和基于平滑度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构的影响，并比较了由于采样不足而导致的不同时间分辨率下的重构精度。使用来自大鼠脑标本的模拟和实验径向黄金角DCE-MRI数据测试了这些方法。结果表明，Huber正则化与基于总变异的模型产生相似的重构精度，但是计算时间明显更快。我们还研究了空间正则化对重构的影响，并比较了由于采样不足而导致的不同时间分辨率下的重构精度。使用来自大鼠脑标本的模拟和实验径向黄金角DCE-MRI数据测试了这些方法。结果表明，Huber正则化与基于总变异的模型产生相似的重构精度，但是计算时间明显更快。我们还研究了空间正则化对重构的影响，并比较了由于采样不足而导致的不同时间分辨率下的重构精度。使用来自大鼠脑标本的模拟和实验径向黄金角DCE-MRI数据测试了这些方法。结果表明，Huber正则化与基于总变异的模型产生相似的重构精度，但是计算时间明显更快。