Abstract
Breast tomosynthesis is different from Computer Tomographic(CT) with its limit projection angle. In other words, breast tomosynthesis can be viewed as the undetermined system. Applying conventional CT reconstruction technique like “Filter Back Projection(FBP)” to tomosynthesis will lead to undesirable strip and phantom artifacts. Thus, the model based iterative algorithms, which carefully adjust the image in each iteration would be more suitable for tomosynthesis image reconstruction. Thanks for the booming development of compressive sensing, new class of image reconstruction method shows more promise in reconstructing a three dimensional image from limit angular, or low dose computer tomography. This kind of method such as total variation, dictionary learning also benefit the breast tomosynthesis. Alternating directional method of multiplier(ADMM) is an algorithm that can handle many kinds of optimization problem. The wide variety of uses is one of its big attractions so far. In this thesis, we propose an image reconstruction algorithm based on ADMM framework and use l0-norm smoothing image as prior, which impose the sparsity constraint to the image. To bridge the gap of convergence rate of existing state-of-art iterative algorithms, we use the adaptive parameter in the gradient descent part of our algorithm and backtracking to ensure the convergence. The results from the simulation experiment shows that our proposed algorithm gives the good image quality under the assumption of strong sparsity of the image to be reconstructed.