A model-based image reconstruction algorithm for near real-time transcranial photoacoustic imaging

Abstract

Photoacoustic imaging is a promising hybrid imaging modality that can visualize both microscopic and macroscopic structures and oxygenation changes in brain function. Phase aberration caused by the skull is a major barrier for high-quality photoacoustic imaging of the brain. Time-reversal methods have been used to address this issue, though they rely on solving the full-wave equation, and is therefore computationally heavy and slow. Herein, a near real-time model-based image reconstruction algorithm is proposed. The acoustic forward model is mathematically described as a model matrix and image reconstructions are performed by employing the pseudoinverse of the model matrix and calculating the initial pressure distribution. The pseudoinverse matrix only needs to be computed once for the same experimental setup and acoustic medium and is obtained offline prior to imaging. The proposed algorithm shows equivalent image quality but considerably faster reconstruction time (>40 times faster), compared to the time-reversal method and therefore could potentially enable near real-time photoacoustic imaging.