Skip to content

OmidChaghaneh/PyQuantUS

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

235 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

PyQuantUS

PyQuantUS is the pip-accessible backend engine for QuantUS written in Python. It contains all data loading and analytical capabilities of QuantUS, enabling scalable batch processing through a command-line interface.

Complete software documentation for QuantUS and PyQuantUS can be found here.

Installation for External Use

Requires Python>=3.10.0.

pip install git+https://github.com/TUL-Dev/PyQuantUS

Installation for Developers

Requires Python>=3.10.0. We will refer to the path of such a Python version as $PYTHON below.

Below are steps to create a new python virtual environment for PyQuantUS. Note lines commented with # Unix should only be run on MacOS or Linux while lines commented with # Windows (cmd) should only be run on the Windows command prompt. These commands should be run from the directory of this repository.

$PYTHON -m pip install virtualenv
$PYTHON -m virtualenv .venv
source .venv/bin/activate # Unix
.venv\Scripts\activate # Windows (cmd)
sudo apt-get update & sudo apt-get install python3-dev # Linux
pip install -r requirements.txt

This environment is activated using source .venv/bin/activate | .venv\Scripts\activate and deactivated using the deactivate command.

Linux Users

lzop is required to unzip .tar files generated by Clarius systems. Ensure this is installed using your preferred package manager.

Usage

See the notebooks in the CLI-Demos folder for examples of analysis for different ultrasound systems. Also note slight differences between examples scan-converted (sc) and non-scan-converted images. Data required to reproduce these examples is stored here. When using these notebooks, the Python environment should match the environment created for QuantUS.

UTC Parameter Validation Progress

  • MBF
  • SS
  • SI
  • Attenuation Coefficient
  • Nakagami Parameters (w, u)
  • Backscatter Coefficient
  • Effective Scatterer Diameter
  • Effective Scatterer Concentration

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages

  • Jupyter Notebook 99.3%
  • Other 0.7%