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

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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,...

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Hauptverfasser: Merrem, Andreas D. (Verfasst von) , Zöllner, Frank G. (Verfasst von) , Schad, Lothar R. (Verfasst von)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: June 2013
In: Magnetic resonance imaging
Year: 2013, Jahrgang: 31, Heft: 5, Pages: 771-777
ISSN:1873-5894
DOI:10.1016/j.mri.2012.10.011
Online-Zugang:Verlag, Volltext: http://dx.doi.org/10.1016/j.mri.2012.10.011
Verlag, Volltext: http://www.sciencedirect.com/science/article/pii/S0730725X12004109
Volltext
Verfasserangaben:Andreas D. Merrem, Frank G. Zöllner, Marcel Reich, Arvid Lundervold, Jarle Rorvik, Lothar R. Schad
Beschreibung
Zusammenfassung: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 1.6mm 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.
Beschreibung:Online 7 December 2012
Gesehen am 12.12.2018
Beschreibung:Online Resource
ISSN:1873-5894
DOI:10.1016/j.mri.2012.10.011

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