Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation
For researchers in photoacoustic imaging, this work addresses the challenge of imaging dynamic processes with high spatiotemporal resolution, though it is an incremental extension of prior compressed sensing work.
The authors extended compressed sensing photoacoustic tomography to 4D by coupling spatial reconstruction with motion estimation, achieving improved image quality for dynamic processes. On a dynamic experimental phantom, the method enabled feasible large-scale 3D reconstruction.
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 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 their potential for challenging, large-scale, real-world, three-dimensional 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.