In deep tissue photoacoustic imaging the spatial resolution is inherently limited by the acoustic wavelength. Recently, it was demonstrated that it is possible to surpass the acoustic diffraction limit by analyzing fluctuations in a set of photoacoustic images obtained under unknown speckle illumination patterns. Here, we purpose an approach to boost reconstruction fidelity and resolution, while reducing the number of acquired images by utilizing a compressed sensing computational reconstruction framework. The approach takes into account prior knowledge of the system response and sparsity of the target structure. We provide proof of principle experiments of the approach and demonstrate that improved performance is obtained when both speckle fluctuations and object priors are used. We numerically study the expected performance as a function of the measurement's signal to noise ratio and sample spatial-sparsity. The presented reconstruction framework can be applied to analyze existing photoacoustic experimental data sets containing dynamic fluctuations.
Bibliographical noteFunding Information:
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grants no. 278025, 677909, 681514), and Human Frontiers Science Program. O.K. was supported by an Azrieli Faculty Fellowship. S.G. acknowledge support from the institut Universitaire de France. E.B. acknowledges support from the Ecole Polytechnique Fédérale de Lausanne (EPFL) via a Visiting Professor Fellowship. We thank Dr. Zhilin Zhang for providing the basis for the M-SBL code.
© 2017 Optical Society of America.