Structurally random Fourier domain compressive sampling and frequency domain beamforming for ultrasound imaging. Foroozan, F., Yousefi, R., Sadeghi, P., & Kolios, M. C. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 2111-2115, Aug, 2017. Paper doi abstract bibtex Advances in ultrasound technology have fueled the emergence of Point-Of-Care Ultrasound (PoCU) imaging, including improved ease-of-use, superior image quality, and lower cost ultrasound. One of the approaches that can make the adoption of PoCU universal is to make the data acquisition module as simple as a "stethoscope" while further processing and image construction can be done using cloud-based processors. Toward this goal, we use Structurally Random Matrices (SRM) for compressive sensing of ultrasound data, Fourier sparsifying matrix for recovery in 1D, and frequency domain approach for 2D ultrasound image reconstruction. This approach is demonstrated through wire phantom and in vivo carotid arteries data from ultrasound system using 25%, 12.5%, and 6.25% of the full data rate and ultrasound images of similar perceived quality quantified by Structural Similarity Index Metric (SSIM).
@InProceedings{8081582,
author = {F. Foroozan and R. Yousefi and P. Sadeghi and M. C. Kolios},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Structurally random Fourier domain compressive sampling and frequency domain beamforming for ultrasound imaging},
year = {2017},
pages = {2111-2115},
abstract = {Advances in ultrasound technology have fueled the emergence of Point-Of-Care Ultrasound (PoCU) imaging, including improved ease-of-use, superior image quality, and lower cost ultrasound. One of the approaches that can make the adoption of PoCU universal is to make the data acquisition module as simple as a {"}stethoscope{"} while further processing and image construction can be done using cloud-based processors. Toward this goal, we use Structurally Random Matrices (SRM) for compressive sensing of ultrasound data, Fourier sparsifying matrix for recovery in 1D, and frequency domain approach for 2D ultrasound image reconstruction. This approach is demonstrated through wire phantom and in vivo carotid arteries data from ultrasound system using 25%, 12.5%, and 6.25% of the full data rate and ultrasound images of similar perceived quality quantified by Structural Similarity Index Metric (SSIM).},
keywords = {biomedical ultrasonics;blood vessels;cloud computing;compressed sensing;data acquisition;image reconstruction;image sampling;medical image processing;phantoms;data acquisition module;compressive sensing;Fourier sparsifying matrix;2D ultrasound image reconstruction;in vivo carotid arteries data;structural similarity index metric;point-of-care ultrasound imaging;image quality;Fourier domain compressive sampling;frequency domain beamforming;stethoscope;cloud-based processors;structurally random matrices;wire phantom;Ultrasonic imaging;Sensors;Imaging;Frequency-domain analysis;Array signal processing;Image reconstruction;Sparse matrices;Compressive Sensing;Structurally Random Matrices;Beamforming;Ultrasound Imaging},
doi = {10.23919/EUSIPCO.2017.8081582},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347347.pdf},
}
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