Machine-learning enhanced photoacoustic computed tomography in a limited view configuration

Abstract

Photoacoustic imaging is an emerging optical imaging modality which provides optical absorption contrasts and high resolution in the optical diffusive regime. In photoacoustic computed tomography (PACT), often times the detection of the photoacoustic signal only covers a partial solid angle less than 4π, due to experimental or economic constraints. Incomplete spatial coverage always jeopardizes image quality and resolution, and results in significant artifacts and missing of image features. This problem is referred to as “limited view” and has remained unsolved for decades. In this work, we present a new machine-learning-based method that is specifically designed to compensate for the missing information due to limited view. The robustness and effectiveness of our method were demonstrated using numerical, phantom, and in vivo experiments.