Segmentation-driven image registration- application to 4D DCE-MRI recordings of the moving kidneys.
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.
Erlend
E
Hodneland
Erik A
EA
Hanson
Arvid
A
Lundervold
Jan
J
Modersitzki
Eli
E
Eikefjord
Antonella Z
AZ
Munthe-Kaas
D016477:Artifacts; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics; D005919:Glomerular Filtration Rate / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000033:anatomy & histology / Q000378:metabolism*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008536:Meglumine / Q000493:pharmacokinetics*; D008954:Models, Biological; D009038:Motion; D009942:Organometallic Compounds / Q000493:pharmacokinetics*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
D003287:Contrast Media; D009942:Organometallic Compounds; D008536:Meglumine; C072417:gadoterate meglumine