Skip to content

Data Simulation Tools for Perfusion MRI

This page provides an overview of software tools for simulating contrast-agent based perfusion MRI data, which can be valuable for testing and validating perfusion quantification methods.

Overview

Simulation tools allow researchers to generate synthetic perfusion MRI data with known ground truth parameters. These tools are essential for:

  • Validating quantification algorithms
  • Testing the effects of noise, temporal resolution, and other acquisition parameters
  • Developing and benchmarking new analysis methods
  • Training and education

Available Simulation Tools

JSim

Developer: University of Washington, Physiome Project
Website: JSim Website
Description: JSim is a Java-based simulation system for building quantitative numerical models and analyzing them with respect to experimental reference data. It can be used to simulate tracer-kinetic models for DCE-MRI and DSC-MRI.

OSIPI DCE/DSC Simulator

Developer: OSIPI Task Force 1.2
Repository: GitHub Repository
Description: A collection of Jupyter notebooks developed by OSIPI for simulating DCE-MRI and DSC-MRI data with various perfusion models. These notebooks allow users to generate synthetic perfusion time-series with user-defined parameter values.

POSSUM (Physics-Oriented Simulated Scanner for Understanding MRI)

Developer: FMRIB Centre, University of Oxford
Website: POSSUM Website
Description: Although primarily designed for fMRI, POSSUM can be adapted for simulating perfusion MRI with the addition of tracer-kinetic models.

JEMRIS (Jülich Extensible MRI Simulator)

Developer: Forschungszentrum Jülich
Website: JEMRIS Website
Description: JEMRIS is an open-source MRI simulator that includes capabilities for simulating various pulse sequences and can be extended to include contrast agent dynamics.

How to Use Simulation Tools in Perfusion Research

  1. Define physiological parameters: Set tissue-specific parameters (e.g., blood flow, vessel permeability, blood volume) based on literature or experimental values.
  2. Set up acquisition parameters: Configure temporal resolution, noise level, and other MRI acquisition parameters.
  3. Run simulation: Generate synthetic perfusion time-series data.
  4. Apply analysis methods: Test different quantification methods on the simulated data.
  5. Compare results with ground truth: Evaluate the accuracy of quantification methods.

Contributing New Simulation Tools

If you have developed a simulation tool for contrast-agent based perfusion MRI that you would like to add to this list, please see our contribution guidelines.

References

  1. Jelescu IO, et al. Challenges for biophysical modeling of microstructure. J Neurosci Methods. 2020;344:108861.
  2. Sourbron SP, Buckley DL. Classic models for dynamic contrast-enhanced MRI. NMR Biomed. 2013;26(8):1004-27.
  3. Koh TS, et al. Fundamentals of tracer kinetics for dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2011;34(6):1262-76.