coreMRI

coreMRI simulates MRI experiments by numerically solving the Bloch equations on cloud-based GPU resources to provide high-performance, realistic MRI and quantitative MR (qMR) simulations.


Key Features:

  • Cloud-based GPU infrastructure: Executes simulations on scalable cloud instances equipped with GPUs to support high-performance computation.
  • Bloch-equation-based simulation framework: Implements a ground-up numerical solution of the Bloch equations consistent with MR physics literature.
  • Modular architecture: Employs a modular design that enables integration of the Gadgetron reconstruction framework and a Pulse Sequence Designer.
  • Multi-GPU performance and scalability: Has been tested on multi-GPU configurations to reduce execution times for realistic MRI and qMR simulations.
  • Pulse sequence and anatomical model integration: Integrates various pulse sequences and anatomical models into the simulation environment.

Scientific Applications:

  • Virtual MRI scanner: Functions as a virtual MRI scanner and high-performance engine for advanced MR simulations.
  • Simulation-based qMR method development: Supports development and testing of quantitative MR (qMR) methods.
  • Pulse sequence and anatomical model design and testing: Enables design and validation of new pulse sequences and anatomical models.

Methodology:

Numerical solution of the Bloch equations; integration of various pulse sequences and anatomical models into the simulation framework; execution and testing on newer-generation GPUs and multi-GPU configurations.

Topics

Details

License:
Unlicense
Maturity:
Mature
Cost:
Free of charge (with restrictions)
Tool Type:
web application
Operating Systems:
Linux, Windows, Mac
Programming Languages:
JavaScript, Java
Added:
8/9/2019
Last Updated:
6/16/2020

Operations

Publications

Xanthis CG, Aletras AH. coreMRI: A high-performance, publicly available MR simulation platform on the cloud. PLOS ONE. 2019;14(5):e0216594. doi:10.1371/journal.pone.0216594. PMID:31100074. PMCID:PMC6524794.

PMID: 31100074
PMCID: PMC6524794
Funding: - Amazon Web Services: AWS Cloud Credits for Research

Documentation