Code for "International multi-centre performance evaluation of copolymer-in-oil tissue-mimicking material for acoustic and optical imaging applications."

Code and data by: Lina Hacker and Thomas Else. Several others contributed to phantom development. Please see the paper for details.

This is the code used to generate the multispectral optoacoustic tomography (MSOT) figures presented in the paper "International multi-centre performance evaluation of copolymer-in-oil tissue-mimicking material for acoustic and optical imaging applications.

Accessing the data

Data will be uploaded to an online repository when the paper is published.

Setting up the code environment.

This code can be downloaded using a link on the GitHub website, or using the following command:

git clone https://github.com/BohndiekLab/IPASCMultiCentreStudy

To run the code, make sure you have a suitable Python environment set up on your computer. We recommend using a tool like https://www.anaconda.com/, [https://docs.python.org/3/library/venv.html](virtual environments), or https://github.com/astral-sh/uv to manage different Python versions. This analysis was run using Python 3.12.3, with versions of each Python library (e.g. NumPy, patato, etc.) specified in the uv.lock and requirements.txt files.

Option 1: Installing via uv:

If using https://github.com/astral-sh/uv to manage the Python version, you can simply run the following command to restore the Python version.

cd IPASCMultiCentreStudy
uv sync

Option 2: Installing via virtual environments and pip:

Alternatively, you can recreate the Python environment using pip (and ideally use a virtual environment too). For guidance on setting up a virtual environment see https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/. Once you have set up a virtual environment run the following:

cd IPASCMultiCentreStudy
pip install -r requirements.txt

Option 3: Anaconda:

First, I would recommend creating a new Anaconda environment, and then activate it as usual. Once you have done so, you can run the following commands:

cd IPASCMultiCentreStudy
pip install -r requirements.txt

Running the code.

To run the code, I would recommend using Jupyter lab. This will be installed automatically if you follow the guidance above.

jupyter-lab 

This will open an interface, from which you can run apply_analysis.ipynb and image_registration.ipynb.