Optoacoustic image reconstruction: The full inverse problem with variable bases

Optoacoustic imaging was for a long time concerned with the reconstruction of energy density or optical properties. In this work, we present the full inverse problem with respect to optical absorption and diffusion as well as speed of sound and mass density. The inverse problem is solved by an iterative gradient-based optimization procedure. Since the ill-conditioning increases with the number of sought parameters, we propose two approaches to improve the conditioning. The first approach is based on the reduction of the size of the basis for the parameter spaces, that evolves according to the particular characteristics of the solution, while maintaining the flexibility of element-wise parameter selection. The second approach is a material identification technique that incorporates prior knowledge of expected material types and uses the acoustical gradients to identify materials uniquely. We present numerical studies to illustrate the properties and functional principle of the proposed methods. Significant convergence speed-ups are gained by the two approaches countering ill-conditioning. Additionally, we show results for the reconstruction of a mouse brain from in vivo measurements.