Delay and Sum (DAS) is one of the most common beamforming algorithms for photoacoustic imaging (PAI) reconstruction. Based on calculating beamformed signal with simple delaying and summing, DAS can function in a quick response and is quite suitable for real-time PAI. However, high sidelobes and intense artifacts may appear when using DAS due to summing with unnecessary data. In this paper, a beamforming algorithm called Multiple Delay and Sum with Enveloping (multi-DASE) is introduced to solve this problem. Compared to DAS, the multi-DASE algorithm calculates not only the initial value of the beamformed signal but also the complete N-shaped photoacoustic signal for each pixel. Through computer simulation, a phantom experiment and experiment on human finger joint, the multi-DASE algorithm is compared with other beamforming methods in removing artifacts by evaluating the quality of the reconstructed images. Furthermore, by rearranging the calculation sequences, the multi-DASE algorithm can be computing in parallel using GPU acceleration to meet the needs of real-time clinical application.

Adipocyte size, i.e., the cell area of adipose tissue, is correlated directly with metabolic disease risk in obese humans. This study proposes an approach of processing the photoacoustic (PA) signal power spectrum using a deep learning method to evaluate adipocyte size in human adipose tissue. This approach has the potential to provide noninvasive assessment of adipose tissue dysfunction, replacing traditional invasive methods of evaluating adipose tissue via biopsy and histopathology. A deep neural network with fully connected layers was used to fit the relationship between PA spectrum and average adipocyte size. Experiments on human adipose tissue specimens were performed, and the optimal parameters of the deep learning method were applied to establish the relationship between the PA spectrum and average adipocyte size. By studying different spectral bands in the entire spectral range using the deep network, a spectral band mostly sensitive to the adipocyte size was identified. A method of combining all frequency components of PA spectrum was tested to achieve a more accurate evaluation.

Objective: With the growth of interest in different medical study on biological function, non-invasive photoacoustic imaging of biological tissue attracts the interests for researchers. To eliminate the limited angle effect of photoacoustic imaging based on ultrasound linear array, spatially distributed ultrasound sensor array is applied. The accurate sensor array position determines the quality of the imaging results. In this study, we proposed three methods based on photoacoustic and ultrasound signals to enhance the imaging quality using a 256-element full-ring array. Methods: Groups of photoacoustic and ultrasound signals are used to regress the position of each element sensor. Result: In phantom study and mouse brain study, photoacoustic imaging results can both yield details clearly with average error rate of less than 1% (50 μm). Conclusion: The performance of our three methods have proved that they can be potentially applied to other ultrasound-based medical imaging studies with unknown distributed positions of sensor array to enhance the imaging quality. Significance: The proposed methods can contribute to precise biomedical imaging with unknown distributed positions of sensor array in different application scenarios.