A Deep Learning approach to Photoacoustic Wavefront Localization in Deep-Tissue Medium.

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Abstract

Optical photons undergo strong scattering when propagating beyond one mm deep inside biological tissue. Finding the origin of these diffused optical wavefronts is a challenging task. Breaking through the optical diffusion limit, photoacoustic imaging (PAI) provides high-resolution and label-free images of human vasculature with high-contrast due to optical absorption of hemoglobin. In real time PAI, an ultrasound transducer array detects photoacoustic (PA) signals, and B-mode images are formed via delay-and-sum or frequency domain beamforming. Fundamentally, the strength of a PA signal is proportional to the local optical fluence, which decreases with increasing depth due to depth-dependent optical attenuation. This limits the visibility of deep tissue vasculature or other light absorbing photoacoustic targets. To address this practical challenge, an encoder-decoder convolutional neural network architecture was constructed with custom modules, and trained to identify the origin of photoacoustic wavefronts inside an optically scattering deep-tissue medium. A comprehensive ablation study provides strong evidence that each module improves localization accuracy. The network was trained on model-based simulated photoacoustic signals produced by 16,240 blood vessel targets subjected to both optical scattering and Gaussian noise. Test results on 4,600 simulated and five experimental PA signals collected under various scattering conditions show the network can localize the targets with a mean error less than 30 μm (standard deviation 20.9 μm) for the targets below 40 mm imaging depth, and 1.06 mm (standard deviation 2.68 mm) for targets at a depth between 40 mm and 60 mm. The proposed work has broad applications such as diffused optical wavefront shaping, circulating melanoma cell detection, and in real time vascular surgeries (e.g., deep vein thrombosis).