Separation of Mainlobes and Sidelobes in the Ultrasound Image Based on the Spatial Covariance (MIST) and Aperture-Domain Spectrum of Received Signals

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Security related resourcesMainlobeSidelobeSeparation
Overview

MainlobeSidelobeSeparation

Separation of Mainlobes and Sidelobes in the Ultrasound Image Based on the Spatial Covariance (MIST) and Aperture-Domain Spectrum of Received Signals

Code, Results, and Sample Datasets

The underlying focusing function and covariance matrix model for MIST is implemented in both MATLAB (Functions) and Python (Functions.py). The following example scripts/tutorials are provided:

  1. The van-Cittert Zernike theorem is used to obtain the spatial correlation between receiver signals as a function of lag (or receive element offset) in a diffuse scattering medium for the entire point spread function (PSF), the mainlobe components of the PSF, and the sidelobe components of the PSF (theoryBehindMIST.m and theoryBehindMIST.py). As suggested by the name of these scripts, these models for the spatial correlation functions of mainlobes and sidelobes in the ultrasound image lay the foundation behind multi-covariate imaging of subresolution targets (MIST). See the prior work on multicovariate imaging of subresolution targets (MIST):

Morgan, M., Trahey, G., Walker, W. "Multi-covariate imaging of sub-resolution targets." IEEE transactions on medical imaging 38.7 (2019): 1690-1700.

  1. The aforementioned spatial correlation functions are used to model the spatial covariance of received signals as well as the FFT of signals across the receive aperture (modelApertureSpectrum.m and modelApertureSpectrum.py). These scripts will first generate the spatial covariance matrices for the mainlobe, sidelobe, and incoherent noise contributions to the the ultrasound image as used in MIST:

Then, these scripts will used these covariance matrices to generate the spectrum of received signals (FFT taken across the receive aperture):

  1. Finally, we compare the original MIST method to our propose aperture-spectrum-based method for separating the mainlobe and sidelobe contributions to the ultrasound image (compareReconstructedImages.m and compareReconstructedImages.py). For these specific scripts, please download the sample data (FieldII_ChannelData.mat) under the releases tab for this repository, and place that data in the main directory (MainlobeSidelobeSeparation).

Citing this Work

If you use the code/algorithm for research, please cite the SPIE conference proceedings paper:

Ali, R., Dahl, J. "Separation of Mainlobe and Sidelobe Contributions to B-Mode Ultrasound Images Based on the Aperture Spectrum". Manuscript in preparation for SPIE Medical Imaging conference.

You can reference a static version of this code by its DOI number: DOI

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