Iterative CT Reconstruction Via Minimizing Adaptively Reweighted Total Variation
L Zhu*, T Niu, Georgia Institute of Technology, Atlanta, GATH-C-103-3 Thursday 10:30AM - 12:30PM Room: 103
Iterative reconstruction via total variation (TV) minimization has demonstrated great successes in accurate CT imaging from under-sampled projections. When projections are further reduced, over-smoothing artifacts appear in the current reconstruction especially around the structure boundaries. We propose a practical algorithm to improve TV-minimization based CT reconstruction on very few projection data.
The L-0 norm approach is more desirable from the perfective of further reducing the projection views. To overcome the computational difficulty of the non-convex optimization of the L-0 norm, we implement an adaptive weighting scheme to approximate the solution via a series of TV minimizations for practical use in CT reconstruction. The weight on TV is initialized as uniform ones, and is adaptively changed based on the gradient of the reconstructed image from the previous iteration. The iteration stops when a small difference between the weighted TV values is observed on two consecutive reconstructed images.
On the digital Shepp-Logan phantom, the proposed method reduces reconstruction errors in the conventional TV minimization from 7.3% to 1.4% with 20 projections, and from 25% to 6.5% with 15 projections. With 20 projections on the Catphan600 phantom, our method reduces contrast errors in the ROIs from 45 HU to 6 HU. The rise width at the object edges in the image is also reduced by 40%, showing a substantial image resolution improvement.
By adaptively reweighting TV in iterative CT reconstruction, we successfully further reduce the projection number for the same or better image quality. The technique is attractive in the applications of CT reconstruction on a small size of projection data.