Modeling Combined Ultrasound and Photoacoustic Imaging: Simulations aiding Device Development and Deep Learning

Combined ultrasound and photoacoustic (USPA) imaging has attracted several clinical applications due to its ability to simultaneously display structural and molecular information of deep biological tissue in real time. However, the depth dependent optical attenuation and the unknown optical and acoustic heterogeneities, limit the USPA imaging performance, especially from deeper tissue regions. Novel instrumentation, image reconstruction and deep learning methods are currently being explored to improve the USPA image quality. Effective implementation of these approaches requires a reliable USPA simulation tool capable of generating US based anatomical and PA based molecular information. Here, we developed a hybrid USPA simulation platform by integrating finite element models of light and ultrasound propagation. The feasibility of modeling US combined with optical fluence dependent multispectral PA imaging is demonstrated using in silico homogeneous and heterogeneous prostate tissue. The platform allows optimization of device design parameters, such as the aperture size and frequency of light source and ultrasound detector arrays. In addition, the potential of this simulation platform to generative massive USPA datasets aiding the data driven deep-learning enhanced USPA imaging has been validated using both simulations and experiments.