Reconstruction of compressively sensed ultrasound RF echoes by exploiting non-Gaussianity and temporal structure

Richard Porter; Vladislav Tadic; Alin Achim

doi:10.1109/ICIP.2015.7350748

Reconstruction of compressively sensed ultrasound RF echoes by exploiting non-Gaussianity and temporal structure

Richard Porter, Vladislav Tadic, Alin Achim

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

In this paper, we propose a method to solve a compressed sensing problem in the multiple measurement vector model using a mixture of Gaussians prior, inspired by existing sparse Bayesian learning approaches. We show that in the multiple measurement vector model we can take advantage of having multiple samples to learn the properties of the distributions of the sources as part of the reconstruction process, and we show that this method can be applied to significantly improve the reconstruction quality of ultrasound images. We further show that we can also improve the quality of reconstruction by taking advantage of the block structure of ultrasound images, using an existing algorithm for block sparse Bayesian learning.

Original language	English
Title of host publication	2015 IEEE International Conference on Image Processing (ICIP)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	6-10
Number of pages	5
Volume	2015-December
ISBN (Print)	9781479983391
DOIs	https://doi.org/10.1109/ICIP.2015.7350748
Publication status	Published - 9 Dec 2015
Event	2015 IEEE International Conference on Image Processing (ICIP) - Quebec City, ON, Canada Duration: 27 Sep 2015 → 30 Sep 2015

Conference

Conference	2015 IEEE International Conference on Image Processing (ICIP)
Country	Canada
City	Quebec City, ON
Period	27/09/15 → 30/09/15

Keywords

compressed sensing
non Gaussian
ultrasound

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10.1109/ICIP.2015.7350748

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title = "Reconstruction of compressively sensed ultrasound RF echoes by exploiting non-Gaussianity and temporal structure",

abstract = "In this paper, we propose a method to solve a compressed sensing problem in the multiple measurement vector model using a mixture of Gaussians prior, inspired by existing sparse Bayesian learning approaches. We show that in the multiple measurement vector model we can take advantage of having multiple samples to learn the properties of the distributions of the sources as part of the reconstruction process, and we show that this method can be applied to significantly improve the reconstruction quality of ultrasound images. We further show that we can also improve the quality of reconstruction by taking advantage of the block structure of ultrasound images, using an existing algorithm for block sparse Bayesian learning.",

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Porter, R, Tadic, V & Achim, A 2015, Reconstruction of compressively sensed ultrasound RF echoes by exploiting non-Gaussianity and temporal structure. in 2015 IEEE International Conference on Image Processing (ICIP). vol. 2015-December, 7350748, Institute of Electrical and Electronics Engineers (IEEE), pp. 6-10, 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, ON, Canada, 27/09/15. https://doi.org/10.1109/ICIP.2015.7350748

Reconstruction of compressively sensed ultrasound RF echoes by exploiting non-Gaussianity and temporal structure. / Porter, Richard; Tadic, Vladislav ; Achim, Alin.

2015 IEEE International Conference on Image Processing (ICIP). Vol. 2015-December Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 6-10 7350748.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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AU - Achim, Alin

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N2 - In this paper, we propose a method to solve a compressed sensing problem in the multiple measurement vector model using a mixture of Gaussians prior, inspired by existing sparse Bayesian learning approaches. We show that in the multiple measurement vector model we can take advantage of having multiple samples to learn the properties of the distributions of the sources as part of the reconstruction process, and we show that this method can be applied to significantly improve the reconstruction quality of ultrasound images. We further show that we can also improve the quality of reconstruction by taking advantage of the block structure of ultrasound images, using an existing algorithm for block sparse Bayesian learning.

AB - In this paper, we propose a method to solve a compressed sensing problem in the multiple measurement vector model using a mixture of Gaussians prior, inspired by existing sparse Bayesian learning approaches. We show that in the multiple measurement vector model we can take advantage of having multiple samples to learn the properties of the distributions of the sources as part of the reconstruction process, and we show that this method can be applied to significantly improve the reconstruction quality of ultrasound images. We further show that we can also improve the quality of reconstruction by taking advantage of the block structure of ultrasound images, using an existing algorithm for block sparse Bayesian learning.

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T2 - 2015 IEEE International Conference on Image Processing (ICIP)

Y2 - 27 September 2015 through 30 September 2015

ER -

Reconstruction of compressively sensed ultrasound RF echoes by exploiting non-Gaussianity and temporal structure

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Keywords

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