According to the physical mechanism of photoacoustic excitation, we demonstrate that the signals detected by a photoacoustic microscopy can be considered as the superposition of a series of pulses with non-negative weight. Based on this factor, we decompose the photoacoustic signals as a series of basis and employ a non-negative constrained least squares criterion to determine their weights. Since the signal components are highly correlated with the basis but noise is not, the process will keep the signal components, but remove the noise and finally enhance the image. Phantom and in vivo animal experiments validate this method.
Synthetic aperture imaging and virtual point detection have been exploited to extend the depth-ofview of photoacoustic microscopy. The approach is commonly based on a constant assumed sound speed, which reduces image quality. We propose a new self-adaptive technique to estimate the speed of sound for being integrated with this hybrid strategy. It is accomplished through linear regression between the square of time-of-fight detected at individual virtual detectors and the square of their horizontal distances on the focal plane. The imaging results show our proposed method can significantly improve the lateral resolution, imaging intensity and spatial precision for inhomogeneous tissue.
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