Biton Shai, Arbel Nadav, Drozdov Gilad, Gilboa Guy, Rosenthal Amir
Andrew and Erna Viterbi Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Technion City 32000, Haifa, Israel.
Photoacoustics. 2019 Nov 6;16:100142. doi: 10.1016/j.pacs.2019.100142. eCollection 2019 Dec.
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 -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- scheme in enhancing image contrast.
在光声层析成像中，图像重建通常是利用不完整或有噪声的数据来进行的，这会导致重建误差。在这种情况下，通过使用非线性正则化方法，如总变差最小化和基于稀疏性保持的方法，可以显著提高重建精度。在本文中，我们引入了一种基于自适应各向异性总变差正则化的光声图像重建新框架，该框架比传统的总变差正则化更能保留复杂的边界。新方案在血管图像的数值模拟以及实验数据上得到了验证，并且在增强图像对比度方面比总变差方案更有效。
