Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation
Felix Lucka, Nam Huynh, Marta Betcke, Edward Zhang, Paul Beard, Ben, Cox, Simon Arridge

TL;DR
This paper advances 4D photoacoustic tomography by integrating motion estimation with compressed sensing, enabling higher quality imaging of dynamic biological tissues from sub-sampled data.
Contribution
It introduces a joint reconstruction and motion estimation framework that leverages temporal redundancy to improve 4D PAT image quality, demonstrated on simulated and real data.
Findings
Enhanced image quality in 4D PAT using joint reconstruction and motion estimation.
Effective handling of large-scale, real-world 3D dynamic imaging scenarios.
Validated approach with both simulated and experimental data.
Abstract
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 sub-sampled 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 two-dimensional numerical phantom will be used to…
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