We report on a new quantification methodology of optoacoustic tomographic reconstructions under heterogeneous illumination conditions representative of realistic whole-body imaging scenarios. Our method relies on the differences in the spatial characteristics of the absorption coefficient and the optical energy density within the medium. By using sparse-representation based decomposition, we exploit these different characteristics to extract both the absorption coefficient and the photon density within the imaged object from the optoacoustic image. In contrast to previous methods, this algorithm is not based on the solution of theoretical light transport equations and it does not require explicit knowledge of the illumination geometry or the optical properties of the object and other unknown or loosely defined experimental parameters, leading to highly robust performance. The method was successfully examined with numerically and experimentally generated data and was found to be ideally suited for practical implementations in tomographic schemes of varying complexity, including multiprojection illumination systems and multispectral optoacoustic tomography (MSOT) studies of tissue biomarkers.
['Amir Rosenthal', 'Daniel Razansky', 'Vasilis Ntziachristos']
Quantitative Optoacoustic Signal Extraction Using Sparse Signal Representation