2 research outputs found
Interpolation of CT Projections by Exploiting Their Self-Similarity and Smoothness
As the medical usage of computed tomography (CT) continues to grow, the
radiation dose should remain at a low level to reduce the health risks.
Therefore, there is an increasing need for algorithms that can reconstruct
high-quality images from low-dose scans. In this regard, most of the recent
studies have focused on iterative reconstruction algorithms, and little
attention has been paid to restoration of the projection measurements, i.e.,
the sinogram. In this paper, we propose a novel sinogram interpolation
algorithm. The proposed algorithm exploits the self-similarity and smoothness
of the sinogram. Sinogram self-similarity is modeled in terms of the similarity
of small blocks extracted from stacked projections. The smoothness is modeled
via second-order total variation. Experiments with simulated and real CT data
show that sinogram interpolation with the proposed algorithm leads to a
substantial improvement in the quality of the reconstructed image, especially
on low-dose scans. The proposed method can result in a significant reduction in
the number of projection measurements. This will reduce the radiation dose and
also the amount of data that need to be stored or transmitted, if the
reconstruction is to be performed in a remote site
Sparse and redundant signal representations for x-ray computed tomography
Image models are central to all image processing tasks. The great
advancements in digital image processing would not have been made possible
without powerful models which, themselves, have evolved over time. In the past
decade, patch-based models have emerged as one of the most effective models for
natural images. Patch-based methods have outperformed other competing methods
in many image processing tasks. These developments have come at a time when
greater availability of powerful computational resources and growing concerns
over the health risks of the ionizing radiation encourage research on image
processing algorithms for computed tomography (CT). The goal of this paper is
to explain the principles of patch-based methods and to review some of their
recent applications in CT. We review the central concepts in patch-based image
processing and explain some of the state-of-the-art algorithms, with a focus on
aspects that are more relevant to CT. Then, we review some of the recent
application of patch-based methods in CT