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 proceedingConference 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 languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages6-10
Number of pages5
Volume2015-December
ISBN (Print)9781479983391
DOIs
Publication statusPublished - 9 Dec 2015
Event2015 IEEE International Conference on Image Processing (ICIP) - Quebec City, ON, Canada
Duration: 27 Sep 201530 Sep 2015

Conference

Conference2015 IEEE International Conference on Image Processing (ICIP)
CountryCanada
CityQuebec City, ON
Period27/09/1530/09/15

Keywords

  • compressed sensing
  • non Gaussian
  • ultrasound

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