Performance of compressed sensing based image reconstruction for photoacoustic imaging

Abstract

Photoacoustic Imaging (PAI) is an emerging imaging modality that provides high resolution and image contrast. PAI employs Delay-and-Sum (DAS) beamforming in which data on all array elements are processed for image reconstruction. In order to decrease system cost in PAI, it is desired to use fewer samples in image reconstruction. Reducing the number of active elements of the array without changing the aperture decreases the computational cost. However, spatial undersampling results in poor image resolution and contrast, and causes spatial artifacts called grating lobes. Compressed Sensing (CS) is a data completion scheme that alleviates the effects of undersampling using a priori knowledge of the signal of interest and the measurement scheme. We performed simulations and experiments to compare the performances of the CS image reconstruction algorithm and conventional DAS beamforming. We used Full-Width Half Maximum (FWHM) resolution and image contrast ratio (CR) as performance metrics. Simulation results offer improved image resolution and contrast when CS is used. Lateral resolution in DAS beamformed images deteriorates with depth. A lateral resolution of 150 µm is obtained regardless of depth using only a quarter of the transducer elements. However, the resolution of DAS beamformed images ranges between 255 µm to 508 µm as the depth increases. It is also shown that CS suppresses the effects of grating lobes and improves the contrast ratio up to 14 dB. We also presented the experimental verification of the results. We used an ultrasound research scanner, a tunable laser system, and an optoacoustic phantom in the experiments. We experimentally showed that the CS method mitigates the effects of spatial undersampling and outperforms the DAS beamforming method in terms of contrast by 10.81 dB on average. CS method also offers an improved lateral resolution of approximately 350 µm compared to 750 µm in DAS beamforming.