https://github.com/stevenwhitaker/stfr-mwf
Code for reproducing results in "Myelin Water Fraction Estimation Using Small-Tip Fast Recovery MRI".
https://github.com/stevenwhitaker/stfr-mwf
kernel-learning mri myelin-water-fraction scan-design small-tip-fast-recovery
Last synced: 10 months ago
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Code for reproducing results in "Myelin Water Fraction Estimation Using Small-Tip Fast Recovery MRI".
- Host: GitHub
- URL: https://github.com/stevenwhitaker/stfr-mwf
- Owner: StevenWhitaker
- Created: 2019-09-10T14:13:11.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-04-13T13:17:00.000Z (almost 6 years ago)
- Last Synced: 2025-04-03T21:33:42.847Z (10 months ago)
- Topics: kernel-learning, mri, myelin-water-fraction, scan-design, small-tip-fast-recovery
- Language: Julia
- Size: 90.8 KB
- Stars: 5
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# STFR-MWF
This repository contains code
for reproducing the results in the paper:
Steven T. Whitaker, Gopal Nataraj, Jon-Fredrik Nielsen, and Jeffrey A. Fessler. \
Myelin water fraction estimation using small-tip fast recovery MRI. \
Magnetic Resonance in Medicine. 2020;00:1-14. \
## Getting Started
The code in this repository is structured as a Julia module.
It was coded using Julia 1.1.1,
though other versions may work as well.
1. Download or clone this repository.
2. Download the BrainWeb dataset [here](https://brainweb.bic.mni.mcgill.ca/cgi/brainweb1?alias=phantom_1.0mm_normal_crisp&download=1).
- Choose raw byte (unsigned) for the file format.
- Choose none for compression.
- Place the downloaded file in `/STFR-MWF/src/estimation/data/BrainWeb/`,
where `` is where you downloaded this repo on your computer.
(You will have to create the `data` and `BrainWeb` directories.)
3. Download Julia 1.1.1 [here](https://julialang.org/downloads/oldreleases/).
4. Run Julia.
5. Change directories to this repo with
```julia
julia> cd("")
```
6. Install some necessary packages and load the code by running `setup.jl` via
```julia
julia> include("setup.jl")
```
7. Run any function with
```julia
julia> STFRMWF.func() # Replace func with actual function name
```
Steps 1 through 3 only need to be done once,
and steps 4 through 6 only need to be done once each time you start Julia.
(Note that running `setup.jl` again will not reinstall packages,
so will run faster after the first time.)
## Reproducing Results
To reproduce the results in the paper, call the corresponding function.
For example, to reproduce Figure 3 run
```julia
julia> STFRMWF.figure3()
```
Some results are used for both a figure and a table.
For example, Figure 4 and Table 3 use the same data,
so to reproduce both run
```julia
julia> STFRMWF.figure4table3()
```
To get a list of all available functions, run
```julia
julia> STFRMWF.reproducible
```
The first time calling these functions may take a while
(especially the biased CRLB and NNLS results),
but after running once the results will be saved,
so future calls will simply load the results and display them.
## Data
The data used in the paper and in this code is available at
[Deep Blue Data](https://doi.org/10.7302/nw6e-1d66).