  High radiation dose in CT scans increases a lifetime risk of cancer and has
become a major clinical concern. Recently, iterative reconstruction algorithms
with Total Variation (TV) regularization have been developed to reconstruct CT
images from highly undersampled data acquired at low mAs levels in order to
reduce the imaging dose. Nonetheless, TV regularization may lead to
over-smoothed images and lost edge information. To solve this problem, in this
work we develop an iterative CT reconstruction algorithm with edge-preserving
TV regularization to reconstruct CT images from highly undersampled data
obtained at low mAs levels. The CT image is reconstructed by minimizing an
energy consisting of an edge-preserving TV norm and a data fidelity term posed
by the x-ray projections. The edge-preserving TV term is proposed to
preferentially perform smoothing only on non-edge part of the image in order to
avoid over-smoothing, which is realized by introducing a penalty weight to the
original total variation norm. Our iterative algorithm is implemented on GPU to
improve its speed. We test our reconstruction algorithm on a digital NCAT
phantom, a physical chest phantom, and a Catphan phantom. Reconstruction
results from a conventional FBP algorithm and a TV regularization method
without edge preserving penalty are also presented for comparison purpose. The
experimental results illustrate that both TV-based algorithm and our
edge-preserving TV algorithm outperform the conventional FBP algorithm in
suppressing the streaking artifacts and image noise under the low dose context.
Our edge-preserving algorithm is superior to the TV-based algorithm in that it
can preserve more information of fine structures and therefore maintain
acceptable spatial resolution.
