Segmentation-Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys
Segmentation-Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys
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DOI：
10.1109/tip.2014.2315155
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发表时间：
2014-05-01
影响因子：
10.6
通讯作者：
Munthe-Kaas, Antonella Z.
中科院分区：
文献类型：
--
作者：
Hodneland, Erlend;Hanson, Erik A.;Munthe-Kaas, Antonella Z.
肾脏的动态对比增强磁共振成像（DCE-MRI）需要适当的运动校正和分割，以通过药代动力学建模来估计肾小球滤过率。传统上，共配准、分割和药代动力学建模作为单独的处理步骤被顺序应用。在本文中，一个组合的4D模型，同时注册和分割的整个肾脏。为了在数值实验中演示该模型，我们使用归一化梯度作为配准中的数据项，并使用从分割区域的时间过程到用于监督分割的训练集的Mahalanobis距离。通过将此框架应用于由4D图像时间序列组成的输入，我们同时进行运动校正和肾脏和背景的两个区域分割。这种新方法的潜力在来自10名健康志愿者的真实的DCE-MRI数据上得到了证实。
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the kidneys requires proper motion correction and segmentation to enable an estimation of glomerular filtration rate through pharmacokinetic modeling. Traditionally, co-registration, segmentation, and pharmacokinetic modeling have been applied sequentially as separate processing steps. In this paper, a combined 4D model for simultaneous registration and segmentation of the whole kidney is presented. To demonstrate the model in numerical experiments, we used normalized gradients as data term in the registration and a Mahalanobis distance from the time courses of the segmented regions to a training set for supervised segmentation. By applying this framework to an input consisting of 4D image time series, we conduct simultaneous motion correction and two-region segmentation into kidney and background. The potential of the new approach is demonstrated on real DCE-MRI data from ten healthy volunteers.