Skip to content
Code for Constrained Reference Region Models for DCE-MRI (MRM 26530)
MATLAB Julia
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
analysis
data
dataResults
jlfiles
mfiles
.gitignore
LICENSE
_config.yml
a01_DEMO_Linear.m
a02_DEMO_NonLinear.jl
a03_DEMO_Both.m
e01_1_simMaker.m
e01_2a_simProcessNLSQ.jl
e01_2b_simProcessLLSQ.m
e01_3a_collectResults.m
e01_3b_collectInformation.m
e01_x_simAnalyzerStatic.m
e02_1_qibaPrep.m
e02_2a_qibaProcessNLSQ.jl
e02_2b_qibaProcessLLSQ.m
e02_3_qibaDisplayResults.m
e03_1_unzipData.m
e03_2_stripData.m
e03_3_shipToJulia.m
e03_4_qinProcessNLSQ.jl
e03_5_qinProcessLLSQ.m
e03_6_collectResults.m
e03_x_qinCNR.m
e03_x_seeMaps.m
readme.md

readme.md

CRRM

Introduction

This repository contains code for the manuscript:
Ahmed, Z., & Levesque, I. R. (2016). Increased robustness in reference region model analysis of DCE MRI using two-step constrained approaches. Magnetic Resonance in Medicine.

Most of the code is written in MATLAB. There are some parts, specifically those that require non-linear least squares fitting, where Julia is used with the DCEMRI.jl package. Julia was used because it is much faster than Matlab when it comes to non-linear fitting. There is also some R code, particularly for visualizing the results. To identify these files by their extensions: .m is for Matlab, .jl is for Julia, and .R & .Rmd are for R.

Using Julia

Julia is needed to do non-linear fitting. To get running, the steps are:

  • Download Julia
  • Python is also required - if you don't have it, I suggest grabbing Anaconda
  • If you wish to use an IDE with Julia, then check out Juno which runs on Atom. The IDE is practical, but is not required.
  • Install pre-requisites by first running Julia, and entering the following in the Julia command prompt:
Pkg.add("DCEMRI")
Pkg.build("HDF5")
Pkg.build("PyCall")
Pkg.add("Hwloc")
Pkg.update()
  • Now it should be possible to run Julia code by opening the Julia command prompt, navigating to the CRRM folder using cd("path/to/CRRM"), and then using the command: include("scriptName.jl") where scriptName is the name of the script you wish to run.

Using R

R isn't necessary for running any of the code, but it can be used to visualizing the results. We have included two user-interfaces for sifting through the results from the simulation. To get running with R, the suggested steps are:

  • Download & Install R base and RStudio
  • Install the pre-requisite R packages by launching RStudio and entering the following in the command terminal: install.packages(c('ggplot2','Cairo','knitr','shiny')). There are actually more packages that will need to be installed, but that should happen automatically after the next step
  • Open either of the files in ./analysis in RStudio, and then click on 'Run Document' which is above the text-editor area in RStudio (default hotkey: Ctrl+Shift+K in windows)
  • The interface should launch in new window and be usable

Code

Main functions of interest

For those only interested in the implemented models, the MATLAB implementation for the linear models is in ./mfiles/LRRM.m and /mfiles/CLRRM.m. The implementation in Julia is in ./jlfiles.

A quick demo is included in the scripts starting with a01 to a03.

Code for Simulations & In-vivo Data Analysis

This repository also contains the code used for the simulation and in-vivo data analysis, the results of which was presented in the manuscript. The code is organized as a series of scripts, where each series represents a particular experiment (i.e. simulation, phantom, clinical) and is meant to be run in linear succession.

Code was tested on Matlab 2015b, Julia 0.4.5, R 3.32, all running on Windows 7 - 64 bit. An estimated run time for each script is provided, based on how long it takes to run on our lab computer (i7-4770 / 3.40 GHz / 32 Gb RAM). The simulations take approximately 500 mb of disk space and 1 Gb of RAM to do the linear fitting and 2.5 Gb of RAM to do the non-linear fitting. The non-linear fit requires more RAM because of multi-threading - the 2.5 Gb estimate is from using 4 threads. QIN Breast DCE-MRI dataset will use ~4.5 Gb of RAM for certain steps. The Breast data itself takes 8 Gb of space originally, but it is preprocessed into a smaller size so that it only takes ~1Gb of disk space and the original 8Gb of data can be deleted afterwards.

Folders will be created as necessary, and this behaviour can be changed by modifying ./mfiles/DefaultFolders.m

Experiment 01 - Single Voxel Simulation

In this "experiment", the concentration-time curve for a reference tissue and a tumour voxel is simulated. Noise is added to the tumour voxel to create 10,000 noisy copies of the tumour voxels' concentration-time curve. These curves are then downsampled and the Reference Region Models are fitted to the simulated data. Simulation properties can be set in ./mfiles/SimProperties.m.

Files of interest:

Main:
e01_1_simMaker.m
  - creates the simulation data
e01_2a_simProcessNLSQ.jl
  - run by opening Julia command prompt, and using the command: include("path/to/e01_2a_simProcessNLSQ.jl")
  - fits the reference region models to simulated data using non-linear least-squares (i.e. NRRM and CNRRM)
  - results of fitting are saved as .mat files
e01_2b_simProcessLLSQ.m
  - fits the reference region models to simulated data using linear least squares fitting
  - results of fitting are saved as .mat files
e01_3a_collectResults.m
  - compiles a summary of the fits (e.g. mean, std.dev) from e01_2a and e01_2b
  - the summary is exported as a .csv file
e01_3b_collectInformation.m
  - collects additional information from the fitting results, including the run times and the error in the estimated kep,RR

Optional:
e01_x_simAnalyzerStatic.m
  - this script looks at the relationship between error in kepRR and error in CLRRM fit
./analysis/s01_analysisErrKepRR.Rmd
  - a shiny app for visualizing the results of the previous script

A summary of the results of the simulation are saved in ./dataResults/e01-simResults-refY.csv. These can be visualized by running ./analysis/e01_analysis.Rmd in RStudio.

Experiment 02 - QIBA Phantom

For convenience, the QIBA phantom data for the Tofts Model (version 6) is included in ./data/QIBA-ToftsV6/DICOM. Refer to https://sites.duke.edu/dblab/qibacontent/ for more details on this dataset.

Files of interest:

e02_1_qibaPrep.m
  - converts the phantom DCE-MRI data into concentration data
e02_2a_qibaProcessNLSQ.jl
  - fits the non-linear reference region models to the phantom data
e02_2b_qibaProcessLLSQ.m
  - fits the linear reference region models to the phantom data
e02_3_qibaDisplayResults.m
  - displays the results of the LRRM, NRRM and CRRM fits on the QIBA phantom data

Experiment 03 - QIN Breast DCE-MRI Data

Files of interest:

e03_1_unzipData.m
  - unzips the downloaded data for future steps
e03_2_stripData.m
  - removes unnecessary data to reduce file size, and builds the tumour mask
e03_3_shipToJulia.m
  - pre-processed data so that Julia can analyze it with the NRRM and CNRRM
e03_4_qinProcessNLSQ.jl
  - fits the NRRM and CNRRM to the Breast data
e03_5_qinProcessLLSQ.m
  - fir the LRRM and CLRRM to the Breast data
e03_6_collectResults.m
  - collects the results from all the fits and summarizes them

Optional:
e03_x_qinCNR.m
  - Estimates the CNR in the breast datasets
e03_x_seeMaps.m
  - Displays the estimated maps from the fitting steps
  - Might not be fully functional
You can’t perform that action at this time.