Abstract
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.
Domain : Image Processing
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