Sub-aperture Processing Based Adaptive Beamforming for Photoacoustic Imaging.

Delay-and-sum (DAS) beamformers when applied to photoacoustic (PA) image reconstruction produces strong sidelobes due to the absence of transmit focusing. Consequently, DAS PA images are often severely degraded by strong off-axis clutter. For pre-clinical in vivo cardiac PA imaging, the presence of these noise artifacts hampers the detectability and interpretation of PA signals from the myocardial wall, crucial for studying blood-dominated cardiac pathological information and to complement functional information derived from ultrasound imaging. In this paper, we present photoacoustic sub-aperture processing (PSAP), an adaptive beamforming method, to mitigate these image degrading effects. In PSAP, a pair of DAS reconstructed images is formed by splitting the received channel data into two complementary non-overlapping sub-apertures. Then, a weighting matrix is derived by analyzing the correlation between sub-aperture beamformed images and multiplied with the full-aperture DAS PA image to reduce sidelobes and incoherent clutter. We validated PSAP using numerical simulation studies using point target, diffuse inclusion and microvasculature imaging and in vivo feasibility studies on five healthy murine models. Qualitative and quantitative analysis demonstrate improvements in PAI image quality with PSAP when compared to DAS and coherence factor weighted DAS (DASCF). PSAP demonstrated improved target detectability with higher generalized contrast-to-noise (gCNR) in vasculature simulations where PSAP produces 19.61 % and 19.53 % higher gCNR than DAS and DASCF, respectively. Furthermore, PSAP provided higher image contrast quantified using contrast ratio (e.g., PSAP produces 89.26 % and 11.90 % higher contrast ratio than DAS and DASCF in vasculature simulations) and improved clutter suppression.