The limited-view compensation of photoacoustic tomography via deep learning

Photoacoustic (PA) computed tomography (PACT) combines the superiorities of both optical imaging and ultrasound imaging, which is based on the generation of acoustic signal (PA wave) by short pulsed laser light. However, in many applications, the sensor array can only partially enclose the target, resulting in limited-view setup and image reconstruction with severe artifacts. Standard reconstruction always induces artifacts using limited-view signals, which are influenced by limited angle coverage of transducers, finite bandwidth, and uncertain heterogeneous biological tissue. To address these issues, in this paper, a deep learning based approach is proposed to compensate the missing data due to limited view (e.g. only 90 degrees coverage). To acquire the missing data from the other 270 degrees' view, we trained an end-to-end network to recover the limited-view PA data, which have been delayed to form a pressure map in region of interest.