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Image registration in dynamic renal MRI—current status and prospects

Abstract

Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acquired over time for up to several minutes. Therefore, they are hampered by motion, e.g., by pulsation, peristaltic, or breathing motion. This motion can hinder subsequent image analysis to estimate hemodynamic parameters like renal blood flow or glomerular filtration rate (GFR). To overcome motion artifacts in time-resolved renal MRI, a wide range of strategies have been proposed. Renal image registration approaches could be grouped into (1) image acquisition techniques, (2) post-processing methods, or (3) a combination of image acquisition and post-processing approaches. Despite decades of progress, the translation in clinical practice is still missing. The aim of the present article is to discuss the existing literature on renal image registration techniques and show today’s limitations of the proposed techniques that hinder clinical translation. This paper includes transformation, criterion function, and search types as traditional components and emerging registration technologies based on deep learning. The current trend points towards faster registrations and more accurate results. However, a standardized evaluation of image registration in renal MRI is still missing.

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Acknowledgements

This article is based on work from COST Action Magnetic Resonance Imaging Biomarkers for Chronic Kidney Disease (Grant CA16103) (PARENCHIMA), funded by COST (European Cooperation in Science and Technology). http://www.cost.eu. For additional information, please visit PARENCHIMA project website: http://www.renalmri.org.

This research project is partly supported of the Research Campus M2OLIE funded by the German Federal Ministry of Education and Research (BMBF) within the Framework “Forschungscampus: public–private partnership for Innovations” under the funding codes 13GW0092D and 13GW0388A.

The European Commission funding of the InnoRenew CoE project (Grant Agreement #739574) under the Horizon 2020 Widespread-Teaming program and the Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Union of the European Regional Development Fund) is gratefully acknowledged.

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GK: acquisition of data, analysis and interpretation of data, and critical revision. PR: acquisition of data, analysis and interpretation of data, drafting of manuscript, and critical revision. MK: acquisition of data, analysis and interpretation of data, drafting of manuscript, and critical revision. AM: acquisition of data, analysis and interpretation of data, drafting of manuscript, and critical revision. AŠT: analysis and interpretation of data, drafting of manuscript, and critical revision. FGZ: study conception and design, acquisition of data, analysis and interpretation of data, and drafting of manuscript.

Correspondence to Frank G. Zöllner.

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Zöllner, F.G., Šerifović-Trbalić, A., Kabelitz, G. et al. Image registration in dynamic renal MRI—current status and prospects. Magn Reson Mater Phy 33, 33–48 (2020). https://doi.org/10.1007/s10334-019-00782-y

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Keywords

  • Kidney disease
  • Image registration
  • Dynamic MRI
  • DCE-MRI
  • ASL