Software

GrOpt Toolbox

  • The toolbox allows for gradient waveforms to be designed that better utilize available gradient hardware performance, while allowing for additional constraints to be flexibly added to control for a range of desired performances. Additionally, the optimization has been fine-tuned to operate in real-time, allowing for flexible implementation on vendor-agnostic scanner hardware for on-the-fly usage.
  • Loecher M, Middione MJ, Ennis DB. A Gradient Optimization (GrOpt) Toolbox for General Purpose Time-Optimal MRI Gradient Waveform DesignMagn Reson Med. Accepted May 2020. 10.1002/mrm.28384
  • Middione MJ, Loecher M, Moulin K, Ennis DB. Optimization Methods for Magnetic Resonance Imaging Gradient Waveform DesignNMR Biomed. 2020 Apr 27:e4308. doi: 10.1002/nbm.4308. [Epub ahead of print] PMID: 32342560
  • https://github.com/cmr-group/gropt/tree/master

 

Diffusion Recon

 

Heart Rate Simulator

  • This repository contains a cross platform (Matlab and Python) script that creates an artificial trigger, based off user input parameters, that writes to an Arduino Uno importable file.
  • https://github.com/tecork/HR-Sim

 

3D DENSE Plugin for Right Ventricle

 

Non-Convex Velocity Solver for Phase Contrast MRI Data

  • This code is meant to accompany the reconstruction described in "Velocity Reconstruction with Non-Convex Optimization for Low-VENC Phase Contrast MRI." The velocity solver itself is written in C++, accompanying code to set up the example datasets and run the solver is written in Python.
  • Loecher M, Ennis DB. Velocity reconstruction with nonconvex optimization for low-velocity-encoding phase-contrast MRI. Magn Reson Med. 2018;80(1):42-52. PMID: 29130519
  • https://github.com/mloecher/nc_vel_solver

 

Convex Optimized Gradient Waveform Design for 4D Flow MRI*

  • This repository contains sample code to use for generating convex optimized waveforms for 4D-Flow MRI.
  • Loeccher M, Magrath P, Aliotta E, Ennis DB. Time-optimized 4D phase contrast MRI with real-time convex optimization of gradient waveforms and fast excitation methodsMagn Reson Med2019;82(1):213-224. PMID: 30859606
  • https://github.com/mloecher/cvxflow

 

4D Laplacian Phase Unwrapping for 4D Flow MRI

  • This Matlab code accompanies the manuscript below to allow the user to reproduce the results from the digital phantom experiment.
  • Loecher M, Schrauben E, Johnson KM, Wieben O. Phase unwrapping in 4D MR flow with a 4D single-step laplacian algorithm. J Magn Reson Imaging. 2016;43(4):833-42. PMID: 26417641
  • https://github.com/mloecher/4dflow-lapunwrap

 

Cardiac Kinematics Phantom

  • This code simulates mid left ventricular motion in an analytical phantom and generate corresponding DENSE data.
  • Verzhbinsky IA, Perotti LE, Moulin K, Cork TE, Loecher M, Ennis DB. Estimating Aggregate Cardiomyocyte Strain Using In Vivo Diffusion and Displacement Encoded MRI. IEEE Trans Med Imaging. 2019 Aug. PMID: 31398112
  • https://github.com/luigiemp/CardiacKinematicsPhantom

 

Convex Optimized Diffusion Encoding (CODE)*

  • Optimizations for Convex Optimized Diffusion Encoding (CODE) for Diffusion Weighted MRI.
  • Aliotta E, Moulin K, Ennis DB. Eddy current-nulled convex optimized diffusion encoding (EN-CODE) for distortion-free diffusion tensor imaging with short echo timesMagn Reson Med. 2018;79(2):663-672. PMID: 28444802
  • https://github.com/ealiotta/code-gradient-design

 

Eddy Current Nulled Convex Optimized Diffusion Encoding (EN-CODE)*

  • Optimizations for Eddy Current Nulled Convex Optimized Diffusion Encoding (EN-CODE) for Diffusion Weighted MRI with reduced distortions.
  • Aliotta E, Wu HH, Ennis DB. Convex optimized diffusion encoding (CODE) gradient waveforms for minimum echo time and bulk motion-compensated diffusion-weighted MRIMagn Reson Med. 2017;77(2):717-729. PMID: 26900872
  • https://github.com/ealiotta/encode-gradient-design
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* For faster optimizations and/or direct b-value optimization, please use the GrOpt Toolbox.