Optoacoustic model-based inversion using anisotropic adaptive total-variation regularization

Shai Biton; Nadav Arbel; Gilad Drozdov; Guy Gilboa; Amir Rosenthal

doi:10.1016/j.pacs.2019.100142

Optoacoustic model-based inversion using anisotropic adaptive total-variation regularization

Shai Biton, Nadav Arbel, Gilad Drozdov, Guy Gilboa, Amir Rosenthal

Technion-Israel Institute of Technology

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

In optoacoustic tomography, image reconstruction is often performed with incomplete or noisy data, leading to reconstruction errors. Significant improvement in reconstruction accuracy may be achieved in such cases by using nonlinear regularization schemes, such as total-variation minimization and L₁-based sparsity-preserving schemes. In this paper, we introduce a new framework for optoacoustic image reconstruction based on adaptive anisotropic total-variation regularization, which is more capable of preserving complex boundaries than conventional total-variation regularization. The new scheme is demonstrated in numerical simulations on blood-vessel images as well as on experimental data and is shown to be more capable than the total-variation-L₁ scheme in enhancing image contrast.

Original language	English
Article number	100142
Journal	Photoacoustics
Volume	16
DOIs	https://doi.org/10.1016/j.pacs.2019.100142
State	Published - Dec 2019
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2019 The Authors

Funding

We are thankful to Anna M. Randi for providing information on the image used in Section 4 . This work was supported by the Technion Ollendorff Minerva Center . Appendix A

Funders	Funder number
Technion Ollendorff Minerva Center

Keywords

Inversion algorithms
Model-based reconstruction
Optoacoustic imaging
Total variation

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10.1016/j.pacs.2019.100142

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abstract = "In optoacoustic tomography, image reconstruction is often performed with incomplete or noisy data, leading to reconstruction errors. Significant improvement in reconstruction accuracy may be achieved in such cases by using nonlinear regularization schemes, such as total-variation minimization and L1-based sparsity-preserving schemes. In this paper, we introduce a new framework for optoacoustic image reconstruction based on adaptive anisotropic total-variation regularization, which is more capable of preserving complex boundaries than conventional total-variation regularization. The new scheme is demonstrated in numerical simulations on blood-vessel images as well as on experimental data and is shown to be more capable than the total-variation-L1 scheme in enhancing image contrast.",

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AU - Arbel, Nadav

AU - Drozdov, Gilad

AU - Gilboa, Guy

AU - Rosenthal, Amir

PY - 2019/12

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AB - In optoacoustic tomography, image reconstruction is often performed with incomplete or noisy data, leading to reconstruction errors. Significant improvement in reconstruction accuracy may be achieved in such cases by using nonlinear regularization schemes, such as total-variation minimization and L1-based sparsity-preserving schemes. In this paper, we introduce a new framework for optoacoustic image reconstruction based on adaptive anisotropic total-variation regularization, which is more capable of preserving complex boundaries than conventional total-variation regularization. The new scheme is demonstrated in numerical simulations on blood-vessel images as well as on experimental data and is shown to be more capable than the total-variation-L1 scheme in enhancing image contrast.

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KW - Total variation

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Optoacoustic model-based inversion using anisotropic adaptive total-variation regularization

Abstract

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Keywords

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