ITEM METADATA RECORD
Title: COMPRESSED SENSING RECONSTRUCTION OF 3D ULTRASOUND DATA USING DICTIONARY LEARNING
Authors: Lorintiu, Oana
Liebgott, Hervé
Alessandrini, Martino
Bernard, Olivier
Issue Date: Nov-2014
Publisher: IEEE
Host Document: Proc. of IEEE International Conference on Image Processing 2014 pages:1317-1321
Conference: IEEE ICIP
Abstract: In this paper we propose a compressed sensing (CS) method
adapted to 3D ultrasound imaging (US). In contrast to previous work, we propose a new approach based on the use of
learned overcomplete dictionaries. Such dictionaries allow
for much sparser representations of the signals since they are
optimized for a particular class of images such as US images.
We will investigate two undersampling patterns of the 3D US
imaging: a spatially uniform random acquisition and a linewise random acquisition. The latter being extremely interesting for 3D imaging: it would indeed allow skipping the acquisition of many lines among the several thousands required
in 3D acquisitions, thus, speeding up the whole acquisition
process and incrementing the imaging rate. In this study, the
dictionary was learned using the K-SVD algorithm on patches
extracted from a training dataset constituted of simulated 3D
non-log envelope US volumes. Experiments were performed
on a testing dataset made of a simulated 3D US log-envelope
volume not included in the testing dataset. CS reconstruction was performed by removing 20% to 80% of the original
samples according to the two undersampling patterns. Reconstructions using a K-SVD dictionary previously trained dictionary indicate minimal information loss, thus showing the
potential of the overcomplete dictionaries.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Cardiovascular Imaging and Dynamics

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