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Workflow sensitivity of post-processing methods in renal DCE-MRI.

Erik Hanson1, Eli Eikefjord2, Jarle Rørvik3

  • 1Department of Mathematics, University of Bergen, Bergen, Norway.

Magnetic Resonance Imaging
|May 25, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a framework to evaluate dynamic contrast-enhanced MRI (DCE-MRI) post-processing chains for estimating glomerular filtration rate (GFR). Gadolinium concentration and B1 correction methods significantly impact GFR accuracy.

Keywords:
Classification treesDCE-MRIGFRGlomerular filtration rateIohexolKidney

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Area of Science:

  • Medical Imaging
  • Renal Physiology
  • Data Analysis

Background:

  • Estimating renal filtration using dynamic contrast-enhanced MRI (DCE-MRI) involves multiple complex post-processing steps.
  • The vast number of potential post-processing combinations complicates workflow optimization and accuracy assessment.

Purpose of the Study:

  • To introduce a systematic framework for evaluating DCE-MRI post-processing chains.
  • To assess the sensitivity of these workflows to the accuracy of glomerular filtration rate (GFR) estimation.

Main Methods:

  • Explored 692 post-processing combinations in 20 healthy volunteers undergoing DCE-MRI and iohexol clearance for GFR.
  • Utilized classification trees and random forest ensemble learning for evaluating processing chains.

Main Results:

  • Gadolinium concentration calculation and B1 inhomogeneity correction methods most significantly impacted GFR estimation accuracy.
  • Most segmentation methods had minimal impact, with one automated method outperforming manual segmentation.

Conclusions:

  • Classification trees effectively visualize and communicate the influence of different processing steps in renal DCE-MRI.
  • Identified crucial factors within the post-processing workflow impacting GFR estimation accuracy.