KEYWORDS: Education and training, Data modeling, Gallium nitride, Image restoration, Nd:YAG lasers, Deep learning, Data acquisition, Prostate, Photoacoustic spectroscopy, Laser systems engineering
Photoacoustic imaging (PAI) has emerged as a promising technique for various image guidance procedures. While convolutional neural networks (CNNs) trained on simulated radiofrequency (RF) data have been employed for point source reconstruction, their performance on real data remains a challenge. This paper addresses this limitation by introducing a novel deep learning-based method that utilizes a limited amount of experimental laser-diode-based data for the reconstruction of multiple point sources. The proposed approach employs a dual generative adversarial network (Dual-GAN) trained on experimental RF data from a combination of point source images. The Dual-GAN exhibits superior performance compared to the conventional delay-and-sum (DAS) method, demonstrating enhanced image contrast and a reduced full width at half maximum (FWHM). Notably, the axial and lateral localization errors of the Dual-GAN predictions surpass previous studies, measuring 0.028±0.018mm and 0.087±0.096mm, respectively. Additionally, the model demonstrates generalization capability by successfully reconstructing multiple point sources imaged using a different Nd:YAG laser system. This innovative method marks a significant advancement, offering improved accuracy and versatility in PAI applications involving multiple point sources.
Real-time 3-D photoacoustic (PA) imaging plays a significant role in volumetric imaging applications, such as breast imaging where PA has demonstrated significant potential. Challenges in 3-D PA imaging include long data acquisition time and limited compatibility with commonly used data acquisition systems. This paper introduces a new real-time 3-D PA data acquisition system using a matrix array transducer. Furthermore, we present a 3-D Delay and Glow (DAG) method for source localization that extends upon recently developed 2-D DAG. The experimental results show the functionality of the 3-D PA system. The DAG outperformed the conventional delay and sum (DAS) where axial, lateral, and elevational resolutions, respectively, are 0.06±0.00, 0.25±0.15, and 0.24±0.18mm for DAG and 0.14±0.06, 3.87±0.30, and 2.81±0.62mm for DAS.
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