Enhancing compressed sensing 4D photoacoustic tomography by simultaneous motion estimation
SIAM Journal on Imaging Sciences , Volume 11 - Issue 4 p. 2224- 2253
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 subsampled 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 2D numerical phantom will be used to illustrate the main properties of this recently developed joint-image-reconstruction-and-motion-estimation framework, measured data from a dynamic experimental phantom will also be used to demonstrate its potential for challenging, large-scale, real-world, 3D scenarios. The latter only becomes feasible if a carefully designed combination of tailored optimization schemes is employed, which we describe and examine in more detail.
|Compressed sensing, Dynamic imaging, Photoacoustic tomography, Simultaneous motion estimation, Variational regularization|
|SIAM Journal on Imaging Sciences|
|Mathematics and Algorithms for 3D Imaging of Dynamic Processes (nwo/613.009.106)|
|This work was funded by the The Netherlands Organisation for Scientific Research (NWO); grant id nwo/613.009.106 - Mathematics and Algorithms for 3D Imaging of Dynamic Processes|
Lucka, F, Huynh, N, Betcke, M, Zhang, E, Beard, P, Cox, B.T, & Arridge, S. (2018). Enhancing compressed sensing 4D photoacoustic tomography by simultaneous motion estimation. SIAM Journal on Imaging Sciences, 11(4), 2224–2253. doi:10.1137/18M1170066