A unique deep learning network, Deep-E, is proposed, which utilizes 2D training data to solve a 3D problem. The novelty of this simulation method is to generate a 2D matrix in the axial-elevational plane using an arc-shaped transducer element, instead of generating a 3D matrix using the linear transducer arrays. Deep-E exhibited significant resolution improvement on the in vivo human breast data. In addition, we were able to restore deeper vascular structures and remove the noise artifact. We envision that Deep-E will have a significant impact in linear-array-based photoacoustic imaging studies by providing high-speed and high-resolution image enhancement.
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