Python Package for Reflection Ultrasound Computed Tomography (RUCT) Delay And Sum (DAS) Beamforming

Paper : IEEE TUFFC Link
Documentation: Website Link
Data : Zenodo Link

Latest package release:

Public dataset:

The imaging setup is explained in the following paper Lafci, B. et al. Synthetic Transmit Aperture (STA) method for pulse-echo ultrasound was used in data acquisition. All images were reconstructed using Delay And Sum (DAS) algorithm and compounded to create final high contrast images.

This project uses pip package manager. Please run the following command in your terminal to install the package.

pip install git+https://github.com/berkanlafci/pyruct.git

After installing package, the functions can be called using python scripts.

Example scripts to use pyruct package can be found in _examples folder.

For example, delay and sum example on cpu is called with following commands in terminal.

python exampleCpuDAS.py

The example scripts can be written by users.

pyruct package can be imported in python scripts using following line.

import pyruct as pt

After importing the package, the functions can be called with following lines in python script.

usData      = pt.usReader(filePath=filePath) 	# read data
das         = pt.cpuDAS()                      	# create reconstruction object
imageRecon  = das.recon(usData.sigMat)         	# reconstruct image

Data used in the paper is available here.
Test data is publicly available here.

After the download, place the data in "data/rawData/" folder that shares the same root directory with "exampleCpuDAS.py" script that can be run for testing "pyruct".

If you use this package and/or data in your research, please cite the following paper.

@article{lafci2022expediting,
author	= {Lafci, Berkan and Robin, Justine and Deán-Ben, Xosé Luís and Razansky, Daniel},
title 	= {Expediting Image Acquisition in Reflection Ultrasound Computed Tomography},
journal = {IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
year 	= {2022},
volume 	= {69},
number 	= {10},
pages 	= {2837-2848},
doi 	= {10.1109/TUFFC.2022.3172713}
}

This project is supported by Swiss Data Science Center (SDSC) grant C19-04.

This project is licensed under MIT License.