Affiliations: Nuclear and Radiological Engineering and Medical
Physics Programs, The George W. Woodruff School of Mechanical Engineering,
Georgia Institute of Technology, Atlanta, GA, USA
Note: [] Corresponding author: Lei Zhu, Nuclear and Radiological
Engineering and Medical Physics Programs, The George W. Woodruff School of
Mechanical Engineering, Georgia Institute of Technology, 770 State ST.,
Atlanta, GA 30332-0745, USA. Tel.: +1 404 385 3882; Fax: +1 404 894 3733;
E-mail: [email protected]
Abstract: BACKGROUND: 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. OBJECTIVE: We propose a practical algorithm to improve
TV-minimization based CT reconstruction on very few projection data. METHOD: Based on the theory of compressed sensing, the L-0 norm
approach is more desirable to further reduce 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 automatically 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. RESULTS: We evaluate the proposed algorithm on both a digital phantom and a physical phantom. Using 20
equiangular projections, our method reduces reconstruction errors in the
conventional TV minimization by a factor of more than 5, with improved spatial resolution. CONCLUSIONS: By adaptively reweighting TV in iterative CT reconstruction, we successfully
further reduce the projection number for the same or better image quality.
Keywords: Iterative reconstruction, CT, total variation, compressed sensing
