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https://github.com/rocsg/fijirelax
FijiRelax: 3D+t multi-echo spin-echo sequences MRI analysis
https://github.com/rocsg/fijirelax
image-processing imagej imagej-plugins java modeling mri relaxation-time
Last synced: about 2 months ago
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FijiRelax: 3D+t multi-echo spin-echo sequences MRI analysis
- Host: GitHub
- URL: https://github.com/rocsg/fijirelax
- Owner: Rocsg
- License: other
- Created: 2021-02-01T14:02:17.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2023-02-10T13:59:36.000Z (almost 2 years ago)
- Last Synced: 2024-01-29T14:13:21.056Z (11 months ago)
- Topics: image-processing, imagej, imagej-plugins, java, modeling, mri, relaxation-time
- Language: Java
- Homepage: https://imagej.net/plugins/fijirelax
- Size: 7.66 MB
- Stars: 8
- Watchers: 3
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
## Documentation and latest stable build
[](https://javadoc.io/doc/io.github.rocsg/fijirelax)
[](https://search.maven.org/search?q=g:%22io.github.rocsg%22%20AND%20a:%22fijirelax%22)
[](https://doi.org/10.21105/joss.04981)
## Summary
FijiRelax is a generic tool for 3D+t MRI analysis and exploration using multi-echo spin-echo sequences. This work was supported by the French Ministry of Agriculture, France AgriMer, CNIV and IFV, within VITIMAGE and VITIMAGE-2024 projects (program Plan National Dépérissement du Vignoble).
## Funding
This tools are developed in the context of:
- the [Vitimage 1](https://www.plan-deperissement-vigne.fr/recherches/programmes-de-recherche/vitimage) and [Vitimage 2024](https://www.plan-deperissement-vigne.fr/recherches/programmes-de-recherche/vitimage-2024) projects.
- the [Aplim](https://umr-agap.cirad.fr/recherche/projets-de-recherche/aplim) flagship project.
## Plugin features
- Proton density, T1 and T2 maps computation from multi-echo spin-echo sequences (multiple TR and/or TE)
- Parameters estimation by fitting noise-corrected mono- and biexponential decay models
- Automatic correction of spatial drift and deformations for long T1 or T2 sequences
- Exploration of T1/T2 distribution in ROI over time
- Operable through a GUI, or scriptable for batch processing of large datasets
## Using FijiRelax, from GUI to API
FijiRelax has been designed for three types of scientists: i) end-users using a GUI, ii) advanced users able to use a scripting languages to process large number of images, and iii) developers able to adapt and extend the application with new functionalities.
* **End-users using a GUI**: this mode is recommended for scientists who are not specialists in image processing nor programming. Download FijiRelax through the official Fiji release, and follow the step-by-step installation instructions, as well as the hands-on tutorials built on the test dataset hosted at Zenodo [@fijirelaxDataset]. Then, use the graphical user interface to import and process your own Bruker/NIFTI/Custom data, explore the relaxation maps in space and time using the graphical relaxation curve explorer and export your results as 2D/3D/4D TIFF images. This mode is also recommended for studying new datasets or new biological questions. Among the interface features, the plugin provides a graphical explorer to visualize the relaxation curves, and the estimated PD-weighted T1 and T2 distributions over customizable areas of interest. In 5D hypermaps, the distributions at each time-point can be displayed simultaneously, giving access to valuable information on water distribution in tissues and its evolution during the monitoring period.
* **Advanced users**: this mode can be used by scientists with programming skills. Load the sample BeanShell scripts provided in repository in https://github.com/Rocsg/FijiRelax/tree/master/test/Scripts. Select a script by dragging it into the Fiji interface and run the scripts to reproduce the results of the paper figure1: import a dataset, convert it to an HyperMap (figure1-e), compute the parameter maps. Then, adapt these scripts to your needs, including processing your own data and batch-processing multiple experiments.
* **Developers**: this mode is for programmers fluent with Java and Maven. Start by exploring the FijiRelax API: [API Overview](https://javadoc.io/doc/io.github.rocsg/fijirelax/latest/index.html). Build your own tools on top of the FijiRelax library, provided as a jar file hosted at maven central repository ([Artifact](https://search.maven.org/artifact/io.github.rocsg/fijirelax)), by indicating FijiRelax as a dependency in your POM file and run the unit tests. FijiRelax is hosted on a github public repository ([https://github.com/rocsg/fijirelax](https://github.com/rocsg/fijirelax)) and developers can offer to contribute to its development, and extend it by requesting features, or proposing new features.
## Dataset for testing purpose
A comprehensive dataset can be found on Zenodo at [https://doi.org/10.5281/zenodo.4518730](https://doi.org/10.5281/zenodo.4518730)
## Installation
The following video guide you throughout the installation process, and take a first tour of FijiRelax functions.
[](https://www.youtube.com/watch/8jEVQjRbFcU)
In order to install FijiRelax on your computer, please follow these steps:
1\. *(if needed) *Download and install Fiji from https://fiji.sc/ ; start Fiji, and let it automatically update. Then restart Fiji.
2\. Open Fiji, run the **Update manager** (Help > Update). Click on "OK" to close the first popup windows, then click on the button **Manage update sites...**.
3\. In this list, activate **ImageJ-ITK** by checking the corresponding checkboxes. Don't close the window, or reopen it if you read this too late.
4\. Add the **Fijiyama** repository (by clicking on the button **Add update site**, and filling the fields : name = "/plugins/fijiyama", site = https://sites.imagej.net/Fijiyama), then check the associated checkbox. Now you can click on **Close** and apply the modifications.
5\. Restart Fiji: a new **FijiRelax** entry should be available in the menu (Plugins > Analyze"). If not, go back to the Update Manager, and check that the repositories **ImageJ-ITK** and **Fijiyama** are correctly selected.
## Preparing your data
FijiRleax needs a properly formatted dataset:
- Nifti 4D images, or a set of Nifti 3D images
- Dicom dirs with 3D images, or a set of dirs with 2D images
## The interface
FijiRelax's interface has four main panels :
- With the first panel, you can import / open / export data.
- The second panel holds the processing routines.
- The third panel contains the explorer button.
- The fourth panel has additional helper functions.
## Tutorials
**Tutorial part 1: proton density, T1 and T2 time-series from 3D dicom data of a sorgho plant**
[](https://www.youtube.com/watch/nhWRZN9puFg)
**Tutorial part 2: from 4D HyperMaps to time-lapse plant physiology monitoring**
[](https://www.youtube.com/watch/tiJnq_xN-dY)
## HyperMap data structure
The output image is a 4D MR hyperimage. The "channels" slicer helps you to explore the 4th dimension, that is the images computed, and the input spin echo images. In detail :
- Channels 1,2 3 are respectively the M0 map, T1 map, T2 map (see this information in the slice title, just upside the image pixels)
- Channels 4,5, ..... NR-3 are the successive NR repetition times of the "T1 sequence", in increasing order.
- Channels NR-2,..... NR-2+NE are the successive NE echo times of the "T2 sequence", in increasing order.
Unit for the channels 2 and 3 are milliseconds, which means you can use it like it, without any additional conversion.
For time-lapse experiments, one can compute such a 4D MR hyperimage at successive timepoints, and register and combine them in a 5D MR hyperimage (with an additional slicer to walk through time). Registration and data combining can be done using the series registration mode of the [Fijiyama](/plugins/fijiyama) plugin.
## The science behind
This plugin computes M0, T1 and T2 maps pixelwise from a given set of spin-echo sequences, acquired with different repetition times and/or different echo times.
First a 3d registration is computed to align precisely the successive images, using libraries of the [Fijiyama](/plugins/fijiyama) plugin. Then the rice noise level is estimated, and the M0, T1 and T2 parameters are estimated, fitting mono or bi-exponential curves, corrected with the measured rice noise. For more information, see the paper in next section.
## Support
Feature request, issues ? Please post a message on the [the ImageJ forum](https://forum.image.sc/), using the tag `fijirelax` . For precise requests, technical users can use a github issue, or pull-request modifications (see CONTRIBUTE.md)
## Citing this work
- Romain Fernandez and Cédric Moisy, 2022 «FijiRelax: Fast and noise-corrected estimation of MRI relaxation maps in 3D + t» *under review*
## Software dependencies acknowledgements
- Janne Holopainen for LMA
- Johannes Schindelin et al for [Fiji](/software/fiji) (Schindelin et al., 2012)
- Karl Schmidt and Dimiter Prodanov for MRI Analysis Calculator and CurveFitters
## License
The code of FijiRelax is distributed under GPL 2.0 public license and authorship of Romain Fernandez [email protected] io.github.rocsg
It includes code of Janne Holopainen (LMA), released as GPL 2.0 in the context of this repository
This program is an open-source **free software**: it can be redistributed and/or modified under the terms of the **GNU General Public License** as published by the Free Software Foundation ([http://www.gnu.org/licenses/gpl.txt](http://www.gnu.org/licenses/gpl.txt)).
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.