In photoacoustic imaging, as in many high resolution modalities, the major bottle neck is the acquisition time of finely spatially sampled data. In particular for imaging of dynamical processes, this results in incomplete data. In this talk I am going to review the journey towards dynamic photoacoustic imaging we embarked on in our group at UCL . I will consider fast numerical models for wave propagation for the implementation of the forward and the adjoint operators. I will describe different approaches to reconstruction from compressed or subsampled data for acceleration of a static image reconstruction and finally I will discuss fully dynamic image reconstruction using spatio-temporal regularisation within the variational framework.
Joint work with Simon Arridge, Andreas Hauptmann, Felix Lucka, Bolin Pan, Kiko Rullan from Inverse Problems group at UCL and Paul Beard, Ben Cox, Nam Huynh, Bradley Treeby, Edward Zhang from Photoacoustic Imaging Group at UCL