Iterative Reconstruction of Medical Ultrasound Images Using Spectrally Constrained Phase Updates (regular paper). Michailovich, O., Basarab, A., & Kouamé, D. In IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2019), Venice, Italy, 08/04/2019-11/04/2019, pages (electronic medium), http://www.ieee.org/, 2019. IEEE : Institute of Electrical and Electronics Engineers. ![Iterative Reconstruction of Medical Ultrasound Images Using Spectrally Constrained Phase Updates (regular paper) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Image deconvolution is a standard numerical procedure used in medical ultrasound imaging for improving the resolution and contrast of diagnostic sonograms. However, due to the intrinsic bandlimitedness of ultrasound scanners and the adverse effect of measurement noises, image deconvolution is known to be exceedingly sensitive to the errors incurred during inference of the point spread function (PSF) that characterizes the imaging system in use. In this case, even the slightest errors in specification of the PSF are likely to result in significant artifacts, rendering the reconstructed images worthless. To address the aforementioned problem, this paper describes a new method for blind deconvolution of ultrasound images, in which the errors due to inaccuracies in specification of the PSF are eliminated concurrently with estimation of tissue reflectivity directly from its associated radio-frequency data. A derivation and justification of the proposed method are supported by experimental results which demonstrate the effectiveness and viability of the new technique.
@InProceedings{ MiBaKo2019.1,
author = {Michailovich, Oleg and Basarab, Adrian and Kouam\'e, Denis},
title = "{Iterative Reconstruction of Medical Ultrasound Images Using Spectrally Constrained Phase Updates (regular paper)}",
booktitle = "{IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2019), Venice, Italy, 08/04/2019-11/04/2019}",
year = {2019},
publisher = {IEEE : Institute of Electrical and Electronics Engineers},
address = {http://www.ieee.org/},
pages = {(electronic medium)},
language = {anglais},
URL = {https://doi.org/10.1109/ISBI.2019.8759245 - https://oatao.univ-toulouse.fr/26162/},
abstract = {Image deconvolution is a standard numerical procedure used in medical ultrasound imaging for improving the resolution and contrast of diagnostic sonograms. However, due to the intrinsic bandlimitedness of ultrasound
scanners and the adverse effect of measurement noises, image deconvolution is known to be exceedingly sensitive to the errors incurred during inference of the point spread function (PSF) that characterizes the imaging system in
use. In this case, even the slightest errors in specification of the PSF are likely to result in significant artifacts, rendering the reconstructed images worthless. To address the aforementioned problem, this paper describes a
new method for blind deconvolution of ultrasound images, in which the errors due to inaccuracies in specification of the PSF are eliminated concurrently with estimation of tissue reflectivity directly from its associated
radio-frequency data. A derivation and justification of the proposed method are supported by experimental results which demonstrate the effectiveness and viability of the new technique.}
}
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However, due to the intrinsic bandlimitedness of ultrasound\nscanners and the adverse effect of measurement noises, image deconvolution is known to be exceedingly sensitive to the errors incurred during inference of the point spread function (PSF) that characterizes the imaging system in\nuse. In this case, even the slightest errors in specification of the PSF are likely to result in significant artifacts, rendering the reconstructed images worthless. To address the aforementioned problem, this paper describes a\nnew method for blind deconvolution of ultrasound images, in which the errors due to inaccuracies in specification of the PSF are eliminated concurrently with estimation of tissue reflectivity directly from its associated\nradio-frequency data. 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