Segmentation Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys - 2014


Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the kidneys requires correct 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. During 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 have a tendency to used normalized gradients as knowledge term within the registration and a Mahalanobis distance from the time courses of the segmented regions to a coaching set for supervised segmentation. By applying this framework to an input consisting of 4D image time series, we tend to conduct simultaneous motion correction and two-region segmentation into kidney and background. The potential of the new approach is demonstrated on real DCE-MRI knowledge from ten healthy volunteers.

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