Iterative photoacoustic image reconstruction for three-dimensional imaging by conventional linear-array detection with sparsity regularization

摘要

Iterative image reconstruction algorithms have the potential to reduce the computational time required for photoacoustic tomography (PAT). An iterative deconvolution-based photoacoustic reconstruction with sparsity regularization (iDPARS) is presented which enables us to solve large-scale problems. The method deals with the limited angle of view and the directivity effects associated with clinically relevant photoacoustic tomography imaging with conventional ultrasound transducers. Our Graphics Processing Unit (GPU) implementation is able to reconstruct large 3-D volumes (100×100× 100) in less than 10 minutes. The simulation and experimental results demonstrate iDPARS provides better images than DAS in terms of contrast-to-noise ratio and Root-Mean-Square errors.