Sparse-view x-ray CT reconstruction via total generalized variation regularization

Abstract

Sparse-view CT reconstruction algorithms via total variation (TV) optimize the data iteratively on the basis of a noise- and artifact-reducing model, resulting in significant radiation dose reduction while maintaining image quality. However, the piecewise constant assumption of TV minimization often leads to the appearance of noticeable patchy artifacts in reconstructed images. To obviate this drawback, we present a penalized weighted least-squares (PWLS) scheme to retain the image quality by incorporating the new concept of total generalized variation (TGV) regularization. We refer to the proposed scheme as “PWLS-TGV” for simplicity. Specifically, TGV regularization utilizes higher order derivatives of the objective image, and the weighted least-squares term considers data-dependent variance estimation, which fully contribute to improving the image quality with sparse-view projection measurement. Subsequently, an alternating optimization algorithm was adopted to minimize the associative objective function. To evaluate the PWLS-TGV method, both qualitative and quantitative studies were conducted by using digital and physical phantoms. Experimental results show that the present PWLS-TGV method can achieve images with several noticeable gains over the original TV-based method in terms of accuracy and resolution properties.

Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations

“…However, accurately determining all the parameters in optimization is always challenging in the general topic of regularized CT image restoration [5, 46] or reconstruction problems [37, 47]. Similar to other regularized methods, the regularization parameter is very important.…”

A new Mumford–Shah total variation minimization based model for sparse-view x-ray computed tomography image reconstruction

“…A typical example is total variation (TV) based projection onto convex sets (POCS) reconstruction strategy, based on the piecewise constant assumption of the desired image, has shown its effectiveness for dealing with the data insufficiency from sparse-view sampling [8-10]. Furthermore, to address the limitations of the original TV constrain with isotropic edge property, different weighted-TVs were proposed recently [11-16]. …”

Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study

“…For simplicity, the presented algorithm is termed as “MPD-AwTTV”. The contributions of this study can be summarized as follows: (1) we present an AwTTV regularization that involves the anisotropic edge property of the sequential MPCT images for dealing with the dynamic MPCT deconvolution problem with low-dose scan; (2) we propose a heuristic convergent algorithm with a robust solution under the relative root mean square error (rRMSE) metric (Niu et al 2014); (3) we study the performance of the algorithm on both digital XCAT phantom and preclinical porcine data; and (4) we compare the presented MPD-AwTTV algorithm with other existing deconvolution algorithm (Calamante et al 1996, Fang et al 2015) and demonstrate that the presented algorithm can achieve remarkable gains in noise-induced artifacts suppression, edge details preservation and accurate flow-scaled residue function and MPHM estimation.…”