In photoacoustic tomography, the acoustic propagation time across the specimen is the ultimate limit on sequential sampling frequency. Any further speed-up can only be obtained by parallel acquisition and subsampling/compressed sensing. In this talk, we consider the photoacoustic reconstruction problem from compressed/subsampled measurements utilizing the sparsity of photoacoustic data or photoacoustic image in the Curvelet frame. We discuss the relative merits of the two approaches and demonstrate the results on simulated and 3D real data.

This presentation is part of Minisymposium “MS54 - Hybrid Approaches that Combine Deterministic and Statistical Regularization for Applied Inverse Problems (4 parts)”
organized by: Cristiana Sebu (University of Malta) , Taufiquar Khan (Clemson University) .

Authors:

Bolin Pan (University College London)

Marta Betcke (University College London)

Simon Arridge (University College London)

Ben Cox (Department of Medical Physics, University College London)

Felix Lucka (CWI & UCL)

Nam Huynh ()

Keywords:

image compression, image reconstruction, inverse problems, nonlinear optimization