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19 February 2018 Enabling vendor independent photoacoustic imaging systems with asynchronous laser source
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Channel data acquisition, and synchronization between laser excitation and PA signal acquisition, are two fundamental hardware requirements for photoacoustic (PA) imaging. Unfortunately, however, neither is equipped by most clinical ultrasound scanners. Therefore, less economical specialized research platforms are used in general, which hinders a smooth clinical transition of PA imaging. In previous studies, we have proposed an algorithm to achieve PA imaging using ultrasound post-beamformed (USPB) RF data instead of channel data. This work focuses on enabling clinical ultrasound scanners to implement PA imaging, without requiring synchronization between the laser excitation and PA signal acquisition. Laser synchronization is inherently consisted of two aspects: frequency and phase information. We synchronize without communicating the laser and the ultrasound scanner by investigating USPB images of a point-target phantom in two steps. First, frequency information is estimated by solving a nonlinear optimization problem, under the assumption that the segmented wave-front can only be beamformed into a single spot when synchronization is achieved. Second, after making frequencies of two systems identical, phase delay is estimated by optimizing the image quality while varying phase value. The proposed method is validated through simulation, by manually adding both frequency and phase errors, then applying the proposed algorithm to correct errors and reconstruct PA images. Compared with the ground truth, simulation results indicate that the remaining errors in frequency correction and phase correction are 0.28% and 2.34%, respectively, which affirm the potential of overcoming hardware barriers on PA imaging through software solution.
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Yixuan Wu, Haichong K. Zhang, and Emad M. Boctor "Enabling vendor independent photoacoustic imaging systems with asynchronous laser source ", Proc. SPIE 10494, Photons Plus Ultrasound: Imaging and Sensing 2018, 104945M (19 February 2018);

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