Low-dose CBCT ; FDK ; Anisotropic total variation ; Low mAs
Abstract
This study aims to develop an improved Feldkamp-Davis-Kress (FDK) reconstruction algorithm
using anisotropic total variation (ATV) minimization to enhance the image quality of low-dose conebeam
computed tomography (CBCT). The algorithm first applies a filter that integrates the Shepp-
Logan filter into a cosine window function on all projections for impulse noise removal. A total
variation objective function with anisotropic penalty is then minimized to enhance the difference
between the real structure and noise using the steepest gradient descent optimization with
adaptive step sizes. The preserving parameter to adjust the separation between the noise-free and
noisy areas is determined by calculating the cumulative distribution function of the gradient
magnitude of the filtered image obtained by the application of the filtering operation on each
projection. With these minimized ATV projections, voxel-driven backprojection is finally performed
to generate the reconstructed images. The performance of the proposed algorithm was evaluated
with the catphan503 phantom dataset acquired with the use of a low-dose protocol. Qualitative
and quantitative analyses showed that the proposed ATV minimization provides enhanced CBCT
reconstruction images compared with those generated by the conventional FDK algorithm, with a
higher contrast-to-noise ratio (CNR), lower root-mean-square-error, and higher correlation. The
proposed algorithm not only leads to a potential imaging dose reduction in repeated CBCT scans
via lower mA levels, but also elicits high CNR values by removing noisy corrupted areas and by
avoiding the heavy penalization of striking features.