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Curvelet frame and photoacoustic reconstruction

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Curvelet frame and photoacoustic reconstruction


Marta Betcke


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. Motivated by two results on near optimal sparsity of image representation and wave field propagation in Curvelet frame we consider methods for photoacoustic reconstruction under such sparsity assumptions in both image and data domain and discuss the relations between the two.


Marta Betcke is an associate professor in the Department of Computer Science, Centre for Medical Image Computing (CMIC) and Centre for Inverse Problems (CIP) at UCL. The hallmark of Betcke’s research are efficient tomographic reconstruction methods combining the analysis of the forward operator with state of the art optimisation and more recently data driven techniques to tackle high dimensional incomplete data problems such as e.g. joint dual contrast CT, T1/T2 MRI, PAT/US reconstruction and dynamic imaging by exploiting coherences.


2019-10-08 at 3:00 pm (subject to variability)