Full-view photoacoustic tomography using asymmetric distributed sensors optimized with compressed sensing method

Photoacoustic tomography (PAT) is an effective optical biomedical imaging method characterized with non-ionizing and noninvasive, presenting good soft tissue contrast and excellent spatial resolution. To build a full-view photoacoustic (PA) image, numbers of ultrasound sensors are needed to assure the image quality, which will bring difficulties to data acquisition and real-time image display. Compressed sensing (CS) theory breaks the restriction of Nyquist sampling theorem and is capable to rebuild signals with less measurements. In this contribution, we proposed an optimization method to increase image quality of full-view PAT with less ultrasound sensors, which combines the theory of CS and a circularly distributing asymmetric data acquisition frame. The hardware cost can be saved and time efficiency of PAT can be raised without sacrificing the image quality. The feasibility of our proposed method is verified in simulation experiments which yield expected results. Our study might be helpful for clinical medical imaging such as early stage breast cancer detection, endoscopic imaging and in vivo monitoring.