A variational approach to image registration in dynamic contrast-enhanced MRI of the human kidney

Kidney function can be accessed by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measurements which yield spatially resolved maps of physiological parameters like perfusion or filtration. The motion of the kidneys during the scan is a dominant limitation of the measurement quality, and image registration is necessary for accurate quantification. We analyzed the feasibility of applying an algorithm, originally developed for multimodal registration, to kidney perfusion time series. The algorithm uses a variational calculation scheme to align the images. In four out of five data sets, kidney motion could be reduced to below the spatial resolution of the images of 1.6 mm while preserving the enhancement pattern of kidney perfusion. Fitting a pharmacokinetic model to the data showed an average reduction of the Akaike fit error of 10% for the registered data, suggesting more stable parameters. We conclude that this image registration algorithm is feasible for correcting kidney motion in renal DCE-MRI.