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

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

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Article number100142
StatePublished - Dec 2019
Externally publishedYes


  • Inversion algorithms
  • Model-based reconstruction
  • Optoacoustic imaging
  • Total variation


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