Temporal Huber Regularization for DCE-MRI

Journal of Mathematical Imaging and Vision - Tập 62 - Trang 1334-1346 - 2020
Matti Hanhela1, Mikko Kettunen2, Olli Gröhn2, Marko Vauhkonen1, Ville Kolehmainen1
1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
2A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

Tóm tắt

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.

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Journal of Mathematical Imaging and Vision

Tập 62

1334-1346

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