Adaptive depth compensation algorithm for photoacoustic tomography

Abstract

Photoacoustic (PA) tomography is a rapidly developing imaging modality that provides high-contrast, high spatial-resolution images for vessel distributions in tissue. It can be applied to early breast cancer detection, and therefore it will be a valuable method for breast cancer diagnosis. Tissue absorbs and scatters light, and the optical fluence is known to approximately decrease exponentially. The pixels or voxels in a reconstructed PA image represent the level of absorbed optical energy, which is the product of the absorption coefficient and the optical fluence. Therefore, the contrast of tumors in deep tissue decreases because the optical fluence is low. Quantitative photoacoustic image reconstruction has been proposed to resolve this problem, but the process is based on compensating the reconstructed image with a pre-calculated optical fluence distribution. Because the contrast-to-noise-ratio (CNR) in the reconstructed images of deep tissue is low, amplification also magnifies the noise, which decreases the image quality. Here we propose a novel adaptive depth attenuation compensation algorithm that can provide greater imaging depth without degrading the CNR. The proposed method is evaluated by numerical simulation and a phantom experiment. The results of simulation and the phantom experiment indicate that the proposed method performs better than conventional methods.