Medical Imaging Research Laboratory

Overview

A simple and easy-to-use OsiriX/Horos plug-in for analysing Cardiac Diffusion MR (CDMR) images.

CMRDiffTools has been developed focused on addressing specific issues of cardiac images in order to meet the requirements of the scientific community in Cardiovascular Magnetic Resonance (CMR).

Benefits

  • Fully compatible with OsiriX/Horos medical image viewers.
  • Straightforward deployment on clinical infrastructures (PACS system).
  • Easy-to-use interface for simplifying and streamlining processing and analysis tasks.

Features

  • Support of SIEMENS and Philips data.
  • Computational methods for image de-noising and registration.
  • Computation and visualisation of diffusion anisotropy indices and specific maps in cardiac analysis such as helix and transverse angle.
  • Additional Color Look Up Tables (CLUT) for specific maps such as Fractional Anisotropy (FA) and Helix Angle (HA).

Presentations

R&D Team

William A. ROMERO R. Research Engineer.
Magalie VIALLON MR physicist at Hopital Universitaire de Genève, University of Geneva and CHU Saint-Etienne.
Pierre CROISILLE Chairman Department of Radiology at CHU Saint-Etienne - Université Jean-Monnet. Deputy Director at CREATIS.

Research Partners

Martijn Froeling The University Medical Centre Utrecht, Netherlands
Christian T. Stoeck and Sebastian Kozerke Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
Elizabeth Tunnicliffe Centre for Clinical Magnetic Resonance Research, University of Oxford, United Kingdom
Andrew Scott and Pedro Ferreira Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom
Kévin Moulin, Eric Aliotta and Daniel B. Ennis Department of Radiological Sciences, University of California, Los Angeles, CA, USA.

Funding Acknowledgements

LabEx PRIMES

This work has been supported by The LabEx PRIMES program (Laboratory of Excellence in Physics, Radiobiology, Medical Imaging, and Simulation) during the period September 2016 to August 2017.