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Image registration in dynamic renal MRI-current status and prospects.

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Motion artifacts in renal MRI hinder chronic kidney disease analysis. This review discusses image registration techniques, including deep learning, to improve accuracy and speed, but clinical translation remains a challenge due to a lack of standardized evaluation.

Keywords:
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Area of Science:

  • Medical Imaging
  • Nephrology
  • Biomedical Engineering

Background:

  • Magnetic resonance imaging (MRI) is crucial for diagnosing chronic kidney diseases (CKD).
  • Dynamic contrast-enhanced (DCE-) MRI and arterial spin labeling (ASL) assess hemodynamic parameters like renal blood flow and glomerular filtration rate (GFR).
  • Motion artifacts from physiological processes significantly impede accurate analysis of time-resolved renal MRI data.

Purpose of the Study:

  • To review existing literature on renal image registration techniques for mitigating motion artifacts in time-resolved renal MRI.
  • To discuss the limitations of current techniques hindering clinical translation.
  • To explore traditional and emerging registration methods, including deep learning approaches.

Main Methods:

  • Categorization of renal image registration strategies into acquisition techniques, post-processing methods, or combined approaches.
  • Analysis of traditional registration components: transformation, criterion function, and search types.
  • Inclusion of emerging deep learning-based registration technologies.

Main Results:

  • Various strategies exist to address motion artifacts in renal MRI, including image registration.
  • Deep learning-based methods show promise for faster and more accurate registrations.
  • Despite progress, a significant gap remains between proposed techniques and clinical practice.

Conclusions:

  • Standardized evaluation protocols are critically needed for renal MRI image registration.
  • Overcoming motion artifacts is key to improving hemodynamic parameter estimation in CKD.
  • Further research and standardization are required to facilitate the clinical translation of advanced renal MRI registration techniques.