Efficient block-sparse model-based algorithm for photoacoustic image reconstruction

The model-based algorithm for photoacoustic imaging (PAI) has been proved to be stable and accurate. However, its reconstruction is computationally burdensome which limits its application in the practical PAI. In this paper, we proposed a block-sparse discrete cosine transform (BS-DCT) model-based PAI reconstruction algorithm in order to improve the computational efficiency of the model-based PAI reconstruction. We adopted the discrete cosine transform (DCT) to eliminate the minor coefficients and reduce the data scale. A block-sparse based iterative method was proposed to accomplish the image reconstruction. Due to its block independent nature, we used the CPU-based parallel calculation implementation to accelerate the reconstruction. During the iterative reconstruction, the number of required iterations was reduced by adopting the fast-converging optimization Barzilai-Borwein method. The numerical simulations and in-vitro experiments were carried out. The results has shown that the reconstruction quality is equivalent to the state-of-the-art iterative algorithms. Our algorithm requires less number of iterations with a reduced data scale and significant acceleration through the parallel calculation implementation. In conclusion, the BS-DCT algorithm may be an effectively accelerated practical algorithm for the PAI reconstruction.