Compressed sensing reconstruction of 3D ultrasound data using dictionary learning

O. Lorintiu 1 H. Liebgott 2 M. Alessandrini 3 Olivier Bernard 1 Denis Friboulet 1
1 Images et Modèles
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
2 Imagerie Ultrasonore
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
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 line-wise 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.
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https://hal.archives-ouvertes.fr/hal-01117604
Contributor : Béatrice Rayet <>
Submitted on : Tuesday, February 17, 2015 - 2:14:48 PM
Last modification on : Wednesday, November 20, 2019 - 2:42:40 AM

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O. Lorintiu, H. Liebgott, M. Alessandrini, Olivier Bernard, Denis Friboulet. Compressed sensing reconstruction of 3D ultrasound data using dictionary learning. IEEE International Conference on Image Processing (ICIP) 2014, Oct 2014, Paris, France. pp.1317-1321, ⟨10.1109/ICIP.2014.7025263⟩. ⟨hal-01117604⟩

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