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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 toimprove upon this: images with good spatial resolution and contrast can beobtained from suitably sub-sampled PAT data acquired by novel acoustic scanningsystems if sparsity-constrained image reconstruction techniques such as totalvariation regularization are used. Now, we show how a further increase of imagequality can be achieved for imaging dynamic processes in living tissue (4DPAT). The key idea is to exploit the additional temporal redundancy of the databy coupling the previously used spatial image reconstruction models withsparsity-constrained motion estimation models. While simulated data from atwo-dimensional numerical phantom will be used to illustrate the mainproperties of this recently developedjoint-image-reconstruction-and-motion-estimation framework, measured data froma dynamic experimental phantom will also be used to demonstrate their potentialfor challenging, large-scale, real-world, three-dimensional scenarios. Thelatter only becomes feasible if a carefully designed combination of tailoredoptimization schemes is employed, which we describe and examine in more detail.

Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation