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

• Main Authors: Shai Biton, Nadav Arbel, Gilad Drozdov, Guy Gilboa, Amir Rosenthal
• Format: Article
• Language: English
• Published: Elsevier 2019-12-01
• Series: Photoacoustics
• Online Access: http://www.sciencedirect.com/science/article/pii/S2213597918300351

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. Keywords: Optoacoustic imaging, Total variation, Inversion algorithms, Model-based reconstruction