An algorithm using projection onto subspace of prior distributions for long-wavelength sound wave CT

The stationary long-wavelength sound wave computed tomography is a nonlinear inverse problem that requires the use of prior information of the object. However, the prior assumptions that are usually used in similar inverse problems are more or less inappropriate. In this paper, a new reconstruction algorithm using the prior information is proposed and compared with subspace regularization method and Marquardt reconstruction algorithms. The simulation shows that the proposed algorithm can give a better reconstructed result whether the actual distribution is compatible or incompatible with the prior distributions.