Abstract
In this paper we present an automated, unsupervised, data-driven approach to assess renal function from 3D DCE-MR images. Applying independent component analysis to four different data sets acquired at different field strengths and with different measurement techniques, we show that functional regions in the human kidney can be recovered by a subset of independent components. Time intensity curves, reflecting perfusion in the kidney can be extracted from the processed data. The procedure may allow non-invasive, local assessment of renal function (e.g. glomerular filtration rate, GFR) from the image time series in future.
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Zöllner, F.G., Kocinski, M., Lundervold, A., Rørvik, J. (2007). Assessment of Renal Function from 3D Dynamic Contrast Enhanced MR Images Using Independent Component Analysis. In: Horsch, A., Deserno, T.M., Handels, H., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2007. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71091-2_48
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DOI: https://doi.org/10.1007/978-3-540-71091-2_48
Publisher Name: Springer, Berlin, Heidelberg
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