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This document was primarily created to guide users wishing to reproduce the results from the SRPCA paper: Computational Super-Resolution for Ultrasound Localization Microscopy Through Solving an Inverse Problem (10.1109/TUFFC.2025.3553735). It assumes the user is familiar with the paper material (in particular ULM) and that they are able to run the PALA codes. Furthermore, the use of all the necessary Matlab toolboxes is assumed.
This document reflect the state of the published paper version. If you have any specific questions or concerns, please message me at my gmail address.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Add your files
## Installation
The SRPCA code was developed to integrate into the pipeline provided by PALA. For the overall code structure and ULM parameters, we refer the reader to the original documentation provided by the authors of PALA (see https://github.com/AChavignon/PALA).
However, to make our contribution easier to distinguish from the PALA framework, we have grouped our additions into dedicated folders. Please follow the steps below to integrate the SRPCA code:
1. Move the InVivoTours, InVivoPalaSup and AugmentedInSilico folders into the main PALA directory (should be at the same level in the directory tree as PALA_scripts).
2. Transfer the contents of the Addons folder into the PALA_addons directory.
3. Import the in vivo data into the PALA_data_folder directory.
-[ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
## Data Retrieval
The simulation “In Silico” OPULM PALA dataset is publicly available at: (https://zenodo.org/records/4343435). To reproduce the augmented data used in Figure 3 of the paper, one can run the code located in the AugmentedInSilico/GenSilicoAug directory. Alternatively, the pre-generated augmented datasets for various signal-to-noise ratios (SNRs, in dB) are available in the corresponding SilicoData_*dB folders.
The supplementary data to PALA, used for figures 2, 5, and 7 of the paper, can be accessed at: (https://zenodo.org/records/7883227). The IQ data can be retrieved from the hdf5 files by using the following Matlab code:
data = h5read(strcat(PALA_data_folder,'IQ_001.hdf5'),'/iq');
IQ = squeeze(data(:,:,:,1))+ 1i * squeeze(data(:,:,:,2));
IQ = permute(IQ,[2,3,1]);
```
## Integrate with your tools
-[ ] [Set up project integrations](https://gitlab.irit.fr/minds/these-vassili-pustovalov/srpcapaper/-/settings/integrations)
## Collaborate with your team
-[ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
Use the built-in continuous integration in GitLab.
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-[ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
# Editing this README
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
## Name
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## Description
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## Visuals
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## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
The in vivo “WKY” rat brain dataset used in this study is subject to data usage agreements due to institutional constraints. Please contact Jean-Pierre Remeniéras regarding potential data access under appropriate collaborative or institutional agreements.
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
## Reproducing paper results
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
To facilitate comparison without requiring full re-computation, we provide precomputed result files. Specifically, the MatTrack files, which contain the super-resolved microbubble positions, are available for the SRPCA result on the WKY rat dataset. Please note that MatTrack files were not saved for the in vivo PALA dataset.
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
# Figure 3: simulated “Augmented In Silico” results
The ground truth (GT) in Fig.3.a is directly provided in the PALA data repository and corresponds to the MatOut variable in the PALA_InSilicoFlow_v3_config.mat file.
Use the Fig3b_silicoaug_RSULM.m script to compute Fig.3.b and the Fig3c_silicoaug_SRPCA.m script to compute Fig.3.c. Note that the temporal band-pass filter was not applied to this dataset, as it would have removed a substantial number of microbubbles. The hyperparameters for both RS-ULM and SRPCA were optimized for the 20 dB SNR case and then kept fixed across all other SNR levels for consistency.
Metrics of Table II and Table III can be reproduced by running the SilicoAugMetrics.m code.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
# Figure 6: In Vivo “WKY” rat results
Although the dataset is not publicly available, we provide the scripts used to generate the corresponding results. The Fig.6.a and Fig.6.c were produced by using the Fig6ac_InVivoTours_RSULM.m script. For Fig. 6b and Fig. 6d, we used the Fig6db_InVivoTours_SRPCA.m script.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
# Figure 7: PALA supplementary results
To facilitate data handling, we converted the original HDF5 format of this dataset into the standard Matlab .mat format. The complex-valued IQ data were saved using the script described in the section above.
Use the Fig7db_InVivoPala_SRPCA.m script to compute the results of Fig.7.b. and Fig.7.d.
Regarding the results of Fig.7.a. and Fig.7.d. At our request, the first authors of 10.5281/zenodo.7883226 provided unofficial processing scripts and hyperparameters for applying RS-ULM to this dataset, in order to reproduce the figure shown in the associated README file. As noted in https://doi.org/10.1038/s41551-023-01123-0, official reconstruction parameters for ULM are not publicly available. If you wish to access the unofficial code, we recommend contacting the dataset authors directly. While we do not share the scripts themselves, we do provide the results generated using them. However, please note that we cannot guarantee the accuracy or official validity of these outputs.
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
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@@ -89,5 +50,3 @@ Show your appreciation to those who have contributed to the project.
## License
For open source projects, say how it is licensed.
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
