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Deterministic compressive sampling for high-quality image reconstruction of ultrasound tomography

BACKGROUND: A well-known diagnostic imaging modality, termed ultrasound tomography, was quickly developed for the detection of very small tumors whose sizes are smaller than the wavelength of the incident pressure wave without ionizing radiation, compared to the current gold-standard X-ray mammograp...

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Autores principales: Huy, Tran Quang, Tue, Huynh Huu, Long, Ton That, Duc-Tan, Tran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5445364/
https://www.ncbi.nlm.nih.gov/pubmed/28545406
http://dx.doi.org/10.1186/s12880-017-0206-8
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author Huy, Tran Quang
Tue, Huynh Huu
Long, Ton That
Duc-Tan, Tran
author_facet Huy, Tran Quang
Tue, Huynh Huu
Long, Ton That
Duc-Tan, Tran
author_sort Huy, Tran Quang
collection PubMed
description BACKGROUND: A well-known diagnostic imaging modality, termed ultrasound tomography, was quickly developed for the detection of very small tumors whose sizes are smaller than the wavelength of the incident pressure wave without ionizing radiation, compared to the current gold-standard X-ray mammography. Based on inverse scattering technique, ultrasound tomography uses some material properties such as sound contrast or attenuation to detect small targets. The Distorted Born Iterative Method (DBIM) based on first-order Born approximation is an efficient diffraction tomography approach. One of the challenges for a high quality reconstruction is to obtain many measurements from the number of transmitters and receivers. Given the fact that biomedical images are often sparse, the compressed sensing (CS) technique could be therefore effectively applied to ultrasound tomography by reducing the number of transmitters and receivers, while maintaining a high quality of image reconstruction. METHODS: There are currently several work on CS that dispose randomly distributed locations for the measurement system. However, this random configuration is relatively difficult to implement in practice. Instead of it, we should adopt a methodology that helps determine the locations of measurement devices in a deterministic way. For this, we develop the novel DCS-DBIM algorithm that is highly applicable in practice. Inspired of the exploitation of the deterministic compressed sensing technique (DCS) introduced by the authors few years ago with the image reconstruction process implemented using l (1) regularization. RESULTS: Simulation results of the proposed approach have demonstrated its high performance, with the normalized error approximately 90% reduced, compared to the conventional approach, this new approach can save half of number of measurements and only uses two iterations. Universal image quality index is also evaluated in order to prove the efficiency of the proposed approach. CONCLUSIONS: Numerical simulation results indicate that CS and DCS techniques offer equivalent image reconstruction quality with simpler practical implementation. It would be a very promising approach in practical applications of modern biomedical imaging technology.
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spelling pubmed-54453642017-05-30 Deterministic compressive sampling for high-quality image reconstruction of ultrasound tomography Huy, Tran Quang Tue, Huynh Huu Long, Ton That Duc-Tan, Tran BMC Med Imaging Research Article BACKGROUND: A well-known diagnostic imaging modality, termed ultrasound tomography, was quickly developed for the detection of very small tumors whose sizes are smaller than the wavelength of the incident pressure wave without ionizing radiation, compared to the current gold-standard X-ray mammography. Based on inverse scattering technique, ultrasound tomography uses some material properties such as sound contrast or attenuation to detect small targets. The Distorted Born Iterative Method (DBIM) based on first-order Born approximation is an efficient diffraction tomography approach. One of the challenges for a high quality reconstruction is to obtain many measurements from the number of transmitters and receivers. Given the fact that biomedical images are often sparse, the compressed sensing (CS) technique could be therefore effectively applied to ultrasound tomography by reducing the number of transmitters and receivers, while maintaining a high quality of image reconstruction. METHODS: There are currently several work on CS that dispose randomly distributed locations for the measurement system. However, this random configuration is relatively difficult to implement in practice. Instead of it, we should adopt a methodology that helps determine the locations of measurement devices in a deterministic way. For this, we develop the novel DCS-DBIM algorithm that is highly applicable in practice. Inspired of the exploitation of the deterministic compressed sensing technique (DCS) introduced by the authors few years ago with the image reconstruction process implemented using l (1) regularization. RESULTS: Simulation results of the proposed approach have demonstrated its high performance, with the normalized error approximately 90% reduced, compared to the conventional approach, this new approach can save half of number of measurements and only uses two iterations. Universal image quality index is also evaluated in order to prove the efficiency of the proposed approach. CONCLUSIONS: Numerical simulation results indicate that CS and DCS techniques offer equivalent image reconstruction quality with simpler practical implementation. It would be a very promising approach in practical applications of modern biomedical imaging technology. BioMed Central 2017-05-25 /pmc/articles/PMC5445364/ /pubmed/28545406 http://dx.doi.org/10.1186/s12880-017-0206-8 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Huy, Tran Quang
Tue, Huynh Huu
Long, Ton That
Duc-Tan, Tran
Deterministic compressive sampling for high-quality image reconstruction of ultrasound tomography
title Deterministic compressive sampling for high-quality image reconstruction of ultrasound tomography
title_full Deterministic compressive sampling for high-quality image reconstruction of ultrasound tomography
title_fullStr Deterministic compressive sampling for high-quality image reconstruction of ultrasound tomography
title_full_unstemmed Deterministic compressive sampling for high-quality image reconstruction of ultrasound tomography
title_short Deterministic compressive sampling for high-quality image reconstruction of ultrasound tomography
title_sort deterministic compressive sampling for high-quality image reconstruction of ultrasound tomography
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5445364/
https://www.ncbi.nlm.nih.gov/pubmed/28545406
http://dx.doi.org/10.1186/s12880-017-0206-8
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