In this work, we explore different reconstruction algorithms for photoacoustic image reconstruction of spatially resolved projection data. While the commonly used back-projected reconstruction is efficient and fast to compute, it cannot deal with noise that arises during measurements. Therefore, we formulate photoacoustic image reconstruction in a variational framework where we add prior knowledge in terms of Total Generalized Variation. Using this prior knowledge, we can reduce measurement noise and improve the visibility of vessel structures.
|Title of host publication||Proc. SPIE|
|Publication status||Published - 2017|