Adaptive and Quantitative Reconstruction Algorithm for Photoacoustic Tomography

Photoacoustic (PA) tomography is a rapidly developing imaging modality which can provide high contrast and spatial-resolution images of light absorption distribution in tissue. However, the quantitative reconstruction of absorption distribution is still a challenge. In this study, we propose an adaptive and quantitative reconstruction algorithm for reducing amplification of noises and artifacts in deep position due to light fluence compensation. In this method, the quantitative processing is integrated into the iterative reconstruction, and absorption coefficient distribution is iteratively updated. At each iteration step, the residual is calculated from detected PA signals and the signals calculated from a forward model by using the initial pressure which is calculated from the production of voxel value and the light fluence. By minimizing the residual, the reconstructed values are converged to the true absorption coefficient distributions. Since this method uses a global optimized compensation, better CNR can be obtained. The results of simulation and phantom experiment indicate that the proposed method provide better CNR at deep region. We expect that the capability of increasing imaging depth will broaden clinical applications.