The X-ray computer tomography (CT) scanner has been extensively used in medical diagnosis. How to reduce radiation dose exposure while maintain high image reconstruction quality has become a major concern in the CT field. In this paper, we propose a statistical iterative reconstruction framework based on structure tensor total variation regularization for low dose CT imaging. An accelerated proximal forward-backward splitting (APFBS) algorithm is developed to optimize the associated cost function. The experiments on two physical phantoms demonstrate that our proposed algorithm outperforms other existing algorithms such as statistical iterative reconstruction with total variation regularizer and filtered back projection (FBP).
The high utility and wide applicability of x-ray imaging has led to a rapidly increased number of CT scans over the past
years, and at the same time an elevated public concern on the potential risk of x-ray radiation to patients. Hence, a hot
topic is how to minimize x-ray dose while maintaining the image quality. The low-dose CT strategies include modulation
of x-ray flux and minimization of dataset size. However, these methods will produce noisy and insufficient projection
data, which represents a great challenge to image reconstruction. Our team has been working to combine statistical
iterative methods and advanced image processing techniques, especially dictionary learning, and have produced
excellent preliminary results. In this paper, we report recent progress in dictionary learning based low-dose CT
reconstruction, and discuss the selection of regularization parameters that are crucial for the algorithmic optimization.
The key idea is to use a “balancing principle” based on a model function to choose the regularization parameters during
the iterative process, and to determine a weight factor empirically for address the noise level in the projection domain.
Numerical and experimental results demonstrate the merits of our proposed reconstruction approach.
Recently iterative reconstruction algorithms with total variation (TV) regularization have shown its tremendous power in image reconstruction from few-view projection data, but it is much more demanding in computation. In this paper, we propose an accelerated augmented Lagrangian method (ALM) for few-view CT reconstruction with total variation regularization. Experimental phantom results demonstrate that the proposed method not only reconstruct high quality
image from few-view projection data but also converge fast to the optimal solution.
For interior tomography based on truncated Hilbert transform (THT), the recently proposed truncated singular value
decomposition (TSVD) reconstruction method uses a regularization parameter given directly. In this paper, a method of
choosing the regularization parameter is presented based on L-curve to get an optimal regularization parameter in
theoretical sense. Furthermore, we develop a Tikhonov regularization method in comparison to TSVD. Simulation results
indicate that both of the two regularization methods with the optimal regularization parameters have good performances
on the image quality for both cases of noise-free and noisy projections.
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