Abstract
Breast tomosynthesis is an underdetermined system due to the limit angular range. As a result, we tends to use optimization to get better quality of reconstructed image. Recently, for the reconstruction of the sparse image under an undersampled system, we tend to use the l1 norm (Total variation) constraint for the image in designing of the objective function. However, l1 norm constraint is not a smooth function, we need to choose an algorithm that can handle not everywhere-differentiable property. The primal-dual optimization framework proposed by Chambolle and Pock (CP) can handle generic convex optimization problem, including the usage of total variation constraint. In this study, we use the primal-dual framework to solve the convex optimization using in the reconstruction of breast tomosynthesis and reconstruct the breast image by corresponding CP algorithm. The results show that, we can have better reconstructed image by using CP algorithm while comparing with other algorithm that used in the linear model. For the total variation optimization problem, we can easily use the CP algorithm to get edgepreserved and smoothly distributed images. CP algorithm can also avoid getting the divergence reconstructed results by checking the convergence check. In conclusion, using CP algorithm is robust and can perform well for the image reconstruction of the breast tomosynthesis. Keywords: Breast tomosynthesis, iterative image reconstruction, total variation minimization, primal-dual optimization, CP algorithm