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Proceedings Paper

Automatic alignment of renal DCE-MRI image series for improvement of quantitative tracer kinetic studies
Author(s): Darko Zikic; Steven Sourbron; Xinxing Feng; Henrik J. Michaely M.D.; Ali Khamene; Nassir Navab
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Paper Abstract

Tracer kinetic modeling with dynamic contrast enhanced MRI (DCE-MRI) and the quantification of the kinetic parameters are active fields of research which have the potential to improve the measurement of renal function. However, the strong coronal motion of the kidney in the time series inhibits an accurate assessment of the kinetic parameters. Automatic motion correction is challenging due to the large movement of the kidney and the strong intensity changes caused by the injected bolus. In this work, we improve the quantification results by a template matching motion correction method using a gradient-based similarity measure. Thus, a tedious manual motion correction is replaced by an automatic procedure. The only remaining user interaction is reduced to a selection of a reference slice and a coarse manual segmentation of the kidney in this slice. These steps do not present an overhead to the interaction needed for the assessment of the kinetic parameters. In order to achieve reliable and fast results, we constrain the degrees of freedom for the correction method as far as possible. Furthermore, we compare our method to deformable registration using the same similarity measure. In all our tests, the presented template matching correction was superior to the deformable approach in terms of reliability, leading to more accurate parameter quantification. The evaluation on 10 patient data series with 180-230 images each demonstrate that the quantitative analysis by a two-compartment model can be improved by our method.

Paper Details

Date Published: 26 March 2008
PDF: 8 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691432 (26 March 2008); doi: 10.1117/12.771888
Show Author Affiliations
Darko Zikic, Technische Univ. München (Germany)
Steven Sourbron, University Hospitals Munich-Grosshadern (Germany)
Xinxing Feng, Technische Univ. München (Germany)
Henrik J. Michaely M.D., University Hospitals Munich-Grosshadern (Germany)
Ali Khamene, Siemens Corporate Research (United States)
Nassir Navab, Technische Univ. München (Germany)

Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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