A crucial limitation of current high-resolution 3D photoacoustic tomography (PAT) devices that
employ sequential scanning is their long acquisition time. In previous work, we demonstrated how
to use compressed sensing techniques to improve upon this: images with good spatial resolution and
contrast can be obtained from suitably subsampled PAT data acquired by novel acoustic scanning
systems if sparsity-constrained image reconstruction techniques such as total variation regularization
are used. Now, we show how a further increase of image quality can be achieved for imaging
dynamic processes in living tissue (4D PAT). The key idea is to exploit the additional temporal
redundancy of the data by coupling the previously used spatial image reconstruction models with
sparsity-constrained motion estimation models. While simulated data from a 2D numerical phantom
will be used to illustrate the main properties of this recently developed joint-image-reconstructionand-motion-estimation framework, measured data from a dynamic experimental phantom will also
be used to demonstrate its potential for challenging, large-scale, real-world, 3D scenarios. The
latter only becomes feasible if a carefully designed combination of tailored optimization schemes is
employed, which we describe and examine in more detail