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Photoacoustic tomography with incomplete data

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ASC - Approximation, sampling and compression in data science

In photoacoustic tomography, the acoustic propagation time across the specimen constitutes the ultimate limit on sequential sampling frequency. Furthermore, the state-of-the art PAT systems are still remote from realising this limit. Hence, for high resolution imaging problems, the acquisition of a complete set of data can be impractical or even not possible e.g. the underlying dynamics causes the object to evolve faster than measurements can be acquired. To mitigate this problem we revert to parallel data acquisition along with subsampling/compressed sensing techniques. We consider different regularisation assumptions such as edge sparsity, sparsity of image representation and wave field propagation in Curvelet frame as well as learnt regularisation. We discuss the benefits and limitations of the proposed approaches in PAT context



This talk is part of the Isaac Newton Institute Seminar Series series.

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