NDLI: Compressed sensing reconstruction of line-wise sub-sampled 3D echographic images based on dictionary learning: an experimental study

Content Provider	IEEE Xplore Digital Library
Author	Lorintiu, O. Liebgott, H. Bernard, A. Bernard, O. Friboulet, D.
Copyright Year	2015
Description	Author affiliation: INSA-Lyon, Univ. de Lyon, Lyon, France (Lorintiu, O.; Liebgott, H.; Bernard, A.; Bernard, O.; Friboulet, D.)
Abstract	This paper proposes a compressed sensing method based on overcomplete learned dictionaries for experimental 3D ultrasound (US) imaging. Two undersampling patterns suited for 3D US imaging are investigated in ex vivo 3D US volume acquisitions: a spatially uniform random acquisition and a line-wise random acquisition. The overcomplete dictionary was learned using the K-SVD algorithm on patches extracted from 3D US datasets. The CS reconstruction problem was solved through the $l_{1}$ minimization using the spectral projected gradient algorithm and was performed by removing 20% to 80% of the original samples according to the two undersampling patterns. Besides the K-SVD dictionary we include the Fourier basis and the discrete cosine transform to our experiments for comparison. The approach is evaluated on 3D US experimental data acquired from ex vivo pig brains and sheep hearts. Reconstructions from 50% of the samples of the original 3D volume show little information loss in terms of normalized root mean squared errors.
Starting Page	1
Ending Page	4
File Size	610793
Page Count	4
File Format	PDF
e-ISBN	9781479981823
DOI	10.1109/ULTSYM.2015.0157
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-21
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image reconstruction Dictionaries Three-dimensional displays Heart Discrete cosine transforms Imaging Compressed sensing
Content Type	Text
Resource Type	Article

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