| Name | dmri-commit JSON |
| Version |
2.2.0
JSON |
| download |
| home_page | None |
| Summary | Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) |
| upload_time | 2024-04-12 15:03:11 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | ################################################################################ COMMIT software license agreement Version 2, 24 July 2023 ################################################################################ ------------------------------------PREAMBLE------------------------------------ This license agreement (“Agreement”) is a legal agreement between you and the Diffusion Imaging and Connectivity Estimation laboratory (“DICE lab”) for the use of the COMMIT software (“Software”). By downloading and/or using the Software, you hereby accept and agree to all the terms and conditions of this Agreement. As used in this Agreement, “you” refers to any individual or organization that uses the Software. ------------------------------TERMS AND CONDITIONS------------------------------ [1] License Grant Subject to all the terms and conditions of this Agreement, the DICE lab hereby grants you a worldwide, non-exclusive, non-transferable, limited license to copy, use, modify, and redistribute the Software solely for research and educational purposes, free of charge. [2] Commercial use You may not use the Software and/or any work based on or using the Software for any commercial purpose, including but not limited to selling, licensing, distributing, renting, or leasing the Software for profit. If you want to use the Software for commercial purposes, you may contact the DICE lab to obtain a commercial license agreement. The DICE lab may consider offering a commercial license, subject to negotiation of appropriate terms and conditions, including but not limited to the payment of a license fee and compliance with any additional restriction on the use of the Software. [3] Attributions and Acknowledgments You agree to provide an acknowledgement identifying the Software and referencing its use in any publication, presentation, research result, and product related to or arising from the use of the Software. You also agree to give proper attribution to the original Software and reproduce this Agreement in any modified and/or redistributed work based on the Software. [4] Patents If you plan to file a patent application based on or using the Software, you must contact the DICE lab. The DICE lab may have certain rights or restrictions related to such patents that need to be addressed before filing the application. [5] Compliance with Law In exercising your rights under this Agreement, you agree to comply with all applicable governmental laws and regulations, including but not limited to the use, export, and transmission of the Software. You are solely responsible for ensuring that your use of the Software complies with such laws and regulations. The DICE lab is not responsible for any violations of such laws or regulations by you. [6] Warranty The Software is provided "AS IS" without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and non-infringement of third-party rights. The Software may also contain errors and is subject to further development and revision. The DICE lab does not guarantee the accuracy of the Software or any result or data arising from the use of the Software. [7] Disclaimers The Software has been designed for research purposes only and has not been reviewed or approved by the Food and Drug Administration or by any other agency. You acknowledge and agree that clinical applications are neither recommended nor advised. [8] Limitation of Liability In no event shall the DICE lab be liable to any party for any direct, indirect, special, incidental, exemplary, or consequential damages, however caused and under any theory of liability, arising in any way related to the Software, even if the DICE lab has been advised of the possibility of such damages. Except to the extent prohibited by law or regulation, you assume all risk and liability for your use of the Software. You also agree to indemnify and hold harmless the DICE lab from and against all claims, suits, actions, demands, and judgments arising from your use or misuse of the Software. ------------------------------------CONTACT------------------------------------- If you have any question about the terms of this Agreement, you may contact the head of the DICE lab Alessandro Daducci at alessandro.daducci@univr.it |
| keywords |
neuroimaging
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://pypi.org/project/dmri-commit/)
[](#)
[](https://github.com/daducci/COMMIT/blob/master/LICENSE)
[](#)
[](https://ieeexplore.ieee.org/document/6884830)
[](#)
[](#)
[](#)
[](#)
[](#)
[](https://twitter.com/intent/follow?screen_name=ADaducci)
# COMMIT
The reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality. COMMIT stands for *Convex Optimization for Microstructure Informed Tractography* and is a **powerful framework for enhancing the anatomical accuracy of the reconstructions** by combining tractography with microstructural features of the neuronal tissue.
<img align="right" src="https://github.com/daducci/COMMIT/wiki/images/filtering_methods.png" height="225">
**How?** Starting from an input set of candidate fiber-tracts estimated using standard fiber-tracking techniques, COMMIT models the diffusion MRI signal in each voxel of the image as a *linear combination* of the restricted and hindered contributions generated in every location of the brain by these candidate tracts. Then, COMMIT seeks for the effective contribution of each of them such that they globally fit the measured signal at best.
These weights can be *efficiently estimated by solving a convenient linear system*.
Results clearly demonstrated the benefits of the proposed formulation, opening new perspectives for a more quantitative and biologically-plausible assessment of the structural connectivity of the brain. See the [references](https://github.com/daducci/COMMIT/wiki/References) for more information.
<p align="center">
<img src="https://github.com/daducci/COMMIT/wiki/images/COMMIT_example.png" height="450">
</p>
## Main features
- Very efficient: COMMIT is implemented in Python but the core of the algorithm is implemented in C++ and using **multi-thread programming** for efficient parallel computation.
- Accepts and works with **any input tractogram** (i.e. set of fiber tracts).
- Can easily implement and consider **any multi-compartment model** available in the literature: possibility to account for restricted, hindered as well as isotropic contributions into the signal forward model.
- **Low memory** consumption using optimized sparse data structures, e.g. it can easily run on a standard laptop with 8GB RAM a full-brain tractogram from the HCP data (1M fibers, 3 shells, 1.25 mm^3 resolution).
- **Soon**: **GPU implementation** for even faster model fitting.
## Documentation
More information/documentation, as well as a series of tutorials, can be found in the [wiki pages](https://github.com/daducci/COMMIT/wiki/Home).
### Installation
To install COMMIT, refer to the [installation guide](https://github.com/daducci/COMMIT/wiki/Installation).
### Getting started
To get started with the COMMIT framework, have a look at [this tutorial](https://github.com/daducci/COMMIT/wiki/Getting-started), which will guide you through the main steps of the processing.
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