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Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing.

Benjamin Leporq1, Sorina Camarasu-Pop1, Eduardo E Davila-Serrano1

  • 1Université de Lyon, CREATIS, CNRS UMR 5220, Inserm U1044, INSA-Lyon, Université Lyon 1, 69622 Villeurbanne Cedex, France.

Journal of Medical Engineering
|March 24, 2016
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Summary
This summary is machine-generated.

This study presents a new method for 3D liver perfusion mapping using distributed computing on the European Grid Infrastructure (EGI). This approach significantly speeds up Magnetic Resonance Dynamic Contrast Enhanced (MR-DCE) imaging analysis for liver disease research.

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Area of Science:

  • Medical Imaging
  • Computational Science
  • Hepatology

Background:

  • Accurate liver perfusion quantification is crucial for diagnosing and managing chronic liver diseases.
  • Magnetic Resonance Dynamic Contrast Enhanced (MR-DCE) imaging offers a non-invasive method for assessing liver perfusion.
  • Current processing methods can be time-consuming, limiting clinical applicability.

Purpose of the Study:

  • To develop and validate a distributed computing method for 3D liver perfusion parametric mapping using MR-DCE.
  • To assess the reproducibility and speed-up achieved by processing on the European Grid Infrastructure (EGI).
  • To evaluate the correlation of grid-processed results with a local reference method.

Main Methods:

  • A novel MR acquisition protocol was employed for dynamic contrast-enhanced imaging.
  • A pharmacokinetic model (3-parameter, one-compartment) was used to analyze hepatic capillary perfusion.
  • The processing workflow was parallelized and executed on the EGI using Gwendia and MOTEUR.
  • Seven patients (1 healthy, 6 with chronic liver disease) underwent prospective enrollment and liver biopsy.

Main Results:

  • The distributed computing method demonstrated good reproducibility in repeated processing on the EGI.
  • Results from the grid processing showed strong correlation with a local region-of-interest (ROI)-based reference method.
  • Significant speed-up was achieved, ranging from 71 to 242 (average 126), compared to local processing.
  • The method is suitable for research contexts and can be enhanced with higher signal-to-noise ratio (SNR) from 3T MR systems.

Conclusions:

  • Distributed computing applied to MR-DCE perfusion mapping provides substantial speed-up for the quantification step.
  • This approach facilitates further clinical studies in liver disease research.
  • The validated method offers a reproducible and efficient tool for liver perfusion analysis.