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An axially-variant kernel imaging model applied to ultrasound image reconstruction

Florea, Mihai I. and Basarab, Adrian and Kouamé, Denis and Vorobyov, Sergiy A. An axially-variant kernel imaging model applied to ultrasound image reconstruction. (2018) IEEE Signal Processing Letters, 25 (7). 961-965. ISSN 1070-9908

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Official URL: https://doi.org/10.1109/LSP.2018.2824764

Abstract

Existing ultrasound deconvolution approaches unrealistically assume, primarily for computational reasons, that the convolution model relies on a spatially invariant kernel and circulant boundary conditions. We discard both restrictions and introduce an image formation model applicable to ultrasound imaging and deconvolution based on an axially varying kernel, which accounts for arbitrary boundary conditions. Our model has the same computational complexity as the one employing spatially invariant convolution and has negligible memory requirements. To accommodate the state-of-the-art deconvolution approaches when applied to a variety of inverse problem formulations, we also provide an equally efficient adjoint expression for our model. Simulation results confirm the tractability of our model for the deconvolution of large images. Moreover, in terms of accuracy metrics, the quality of reconstruction using our model is superior to that obtained using spatially invariant convolution.

Item Type:Article
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org The original PDF can be found at IEEE Signal Processing Letters (ISSN 1070-9908) website : https://ieeexplore.ieee.org/document/8333807 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
HAL Id:hal-02348274
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Aalto University (FINLAND)
Laboratory name:
Funders:
Academy of Finland (Finlande) - ANR : Agence nationale de la recherche (France) - CIMI : Centre International de Mathématiques et d’Informatique de Toulouse (France)
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Deposited On:08 Oct 2019 12:56

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