simpa 0.4.0

Simulation and Image Processing for Photoacoustic Imaging

Cancer Research UK, Cambridge Institute (CRUK CI) License: UNKNOWN Platform: UNKNOWN Description-Content-Type: text/markdown License-File: LICENSE.md

# README

The Simulation and Image Processing for Photoacoustic Imaging (SIMPA) toolkit.

(!) Alpha Version 0.4.0 (!)

The toolkit is still under development and is thus not fully tested and may contain bugs. Please report any issues that you find in our Issue Tracker: https://github.com/CAMI-DKFZ/simpa/issues. Also make sure to double check all value ranges of the optical and acoustic tissue properties and to assess all simulation results for plausibility.

## SIMPA Install Instructions

The recommended way to install simpa is a manual installation from the GitHub repository, please follow steps 1 - 3:

1. git clone https://github.com/CAMI-DKFZ/simpa.git

2. git checkout master

3. git pull

Now open a python instance in the ‘simpa’ folder that you have just downloaded. Make sure that you have your preferred virtual environment activated 1. cd simpa 2. pip install -r requirements.txt 3. python setup.py install (for developement: python setup.py develop) 4. Test if the installation worked by using python followed by import simpa then exit()

If no error messages arise, you are now setup to use simpa in your project.

You can also install simpa with pip. Simply run:

pip install simpa

You also need to manually install the pytorch library to use all features of SIMPA. To this end, use the pytorch website tool to figure out which version to install: https://pytorch.org/get-started/locally/

## Building the documentation

When the installation went fine and you want to make sure that you have the latest documentation you should do the following steps in a command line:

1. Navigate to the simpa source directory (same level where the setup.py is in)

2. Execute the command sphinx-build -b html -a docs/src docs

3. Find the HTML file in docs/index.html

## External Tools installation instructions

### mcx (Optical Forward Model)

Either download suitable executables or build yourself from the following sources:

http://mcx.space/

In order to obtain access to all custom sources that we implemented, please build mcx yourself from the following mcx Github fork: https://www.github.com/jgroehl/mcx

For the installation, please follow the instructions from the original repository. Please note that there might be compatiblity issues using mcx-cl with the MCX Adapter as this use case is not being tested and supported by the SIMPA developers.

### k-Wave (Acoustic Forward Model)

Please follow the following steps and use the k-Wave install instructions for further (and much better) guidance under:

http://www.k-wave.org/

1. Install MATLAB with the core and parallel computing toolboxes activated at the minimum.

2. Download the kWave toolbox

3. Add the kWave toolbox base path to the toolbox paths in MATLAB

4. Download the kWaveArray addition from the link given in this user forum post http://www.k-wave.org/forum/topic/alpha-version-of-kwavearray-off-grid-sources

5. Add the kWaveArray folder to the toolbox paths in MATLAB as well

6. If wanted: Download the CPP and CUDA binary files and place them inthe k-Wave/binaries folder

7. Note down the system path to the matlab executable file.

## Overview

The main use case for the simpa framework is the simulation of photoacoustic images. However, it can also be used for image processing.

### Simulating photoacoustic images

A basic example on how to use simpa in your project to run an optical forward simulation is given in the samples/minimal_optical_simulation.py file.

### Path Management

As a pipelining tool that serves as a communication layer between different numerical forward models and processing tools, SIMPA needs to be configured with the paths to these tools on your local hard drive. To this end, we have implemented the PathManager class that you can import to your project using from simpa.utils import PathManager. The PathManager looks for a path_config.env file (just like the one we provided in the simpa_examples) in the following places in this order: 1. The optional path you give the PathManager 2. Your $HOME$ directory 3. The current working directory 4. The SIMPA home directory path

## How to contribute

Please find a more detailed description of how to contribute as well as code style references in our developer_guide.md

The SIMPA code is written and maintained on a closed git repository that is hosted on a server of the German Cancer Research Center (DKFZ), Heidelberg, Germany and changes to the develop or master branch are mirrored on Github. As such, only the current master and develop branch of the repository are open source.

To contribute to SIMPA, please fork the SIMPA github repository and create a pull request with a branch containing your suggested changes. The core team developers will then review the suggested changes and integrate these into the code base.

Please make sure that you have included unit tests for your code and that all previous tests still run through.

There is a regular SIMPA status meeting every Friday on even calendar weeks at 10:00 CET/CEST and you are very welcome to participate and raise any issues or suggest new features. If you want to join this meeting, write one of the core developers (see developer_guide.md)

Please see the github guidelines for creating pull requests: https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests

## Performance profiling

Do you wish to know which parts of the simulation pipeline cost the most amount of time? If that is the case then you can use the following commands to profile the execution of your simulation script. You simply need to replace the myscript name with your script name.

python -m cProfile -o myscript.cprof myscript.py

pyprof2calltree -k -i myscript.cprof