[Cone beam CT image iterative reconstruction based on Split-Bregman method]

We propose a new iterative reconstruction method based on split-Bregman method with tight frame regularization for effective and accurate reconstruction of the sparse-view cone beam CT image.

A tight frame was chosen as the regularization term for the objective function, so that the image reconstruction involves only the minimization of an objective function according to the compressed sensing theory. We utilized the split-Bregman method to tackle the task of minimization in three steps: (1) a fast calculation of the forward projection matrix; (2) introducing an intermediate variable to transform the non-differentiated L1 regularization term into the differentiated L2 regularization problem, and solving the target function using conjugate-gradient method; (3) updating the intermediate variable using shrinkage formula from Bregman method.

Digital and physical phantom experimental results suggested that our new approach had great advantages in terms of image quality, reconstruction time, and applicability.

The proposed method can accurately reconstruct CBCT image with limited data to lower the X-ray dose and accelerate the calculation speed in comparison with the POCS method.