The researches on convolutional beamforming for linear-array photoacoustic tomography

The convolutional beamforming algorithm (COBA) which can be easily implemented by fast Fourier transform (FFT) and is suitable for real-time photoacoustic tomography (PAT) is introduced. In order to reveal the imaging effects of COBA, the photoacoustic images reconstructed by COBA, delay-and-sum algorithm (DAS), and minimum variance (MV) are investigated. The results demonstrate that the COBA significantly improves the lateral resolution and the contrast of the photoacoustic images. Moreover, the coherence factor (CF) as an adaptive weighting is used in COBA, the combined method shows that better lateral resolution and contrast of photoacoustic images can be obtained, which can be a potential imaging method for linear-array PAT.