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

• Published in: IEEE transactions on image processing 2014-05, Vol.23 (5), p.2392-2404
• Main Authors: Hodneland, Erlend, Hanson, Erik A., Lundervold, Arvid, Modersitzki, Jan, Eikefjord, Eli, Munthe-Kaas, Antonella Z.
• Format: Article
• Language: English
• Subjects: Applied sciences; Biological and medical sciences; Computer Simulation; Computerized, statistical medical data processing and models in biomedicine; Contrast Media - pharmacokinetics; Detection, estimation, filtering, equalization, prediction; Educational institutions; Exact sciences and technology; Filtration; Glomerular Filtration Rate - physiology; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Information, signal and communications theory; Kidney; Kidney - anatomy & histology; Kidney - metabolism; Kidneys; Licenses; Magnetic Resonance Imaging, Cine - methods; Mathematical models; Medical management aid. Diagnosis aid; Medical sciences; Meglumine - pharmacokinetics; Models, Biological; Motion; Motion segmentation; NMR; Nuclear magnetic resonance; Organometallic Compounds - pharmacokinetics; Pattern recognition; Pattern Recognition, Automated - methods; Recording; Reproducibility of Results; Segmentation; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal, noise; Subtraction Technique; Telecommunications and information theory; Time series; Time series analysis; Training; Transaction processing
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2014.2315155

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.