Beamforming algorithms are widely used for photoacoustic (PA) imaging to reconstruct the initial pressure map. In the reconstruction process, they typically assumed that the imaged biological tissue was a homogeneous medium. However, as biological tissue is generally heterogeneous, the misassumption causes suboptimal image reconstruction. Because it is difficult to predict the heterogeneity of a medium, it was still common to reconstruct images assuming a uniform medium. To solve this problem, we introduce a deep learning-based algorithm that can correct the speed of sound (SoS) aberration in the PA image. We trained a neural network with the multiple simulation datasets and successfully corrected SoS aberrations in a PA in vivo image of the human forearm. We observed that the proposed algorithm effectively suppressed side lobes and noise in the PA image and greatly improves image quality.
https://www.opotek.com/wp-content/uploads/2018/08/opotek-logo-v4-4-325x100-300x93.png 0 0 Katy https://www.opotek.com/wp-content/uploads/2018/08/opotek-logo-v4-4-325x100-300x93.png Katy2021-11-18 23:00:142021-11-18 23:00:14Deep learning-based speed of sound aberration correction in photoacoustic images