Cone-beam computed tomography based on truncated adaptive-weight total variation

Liu, Yuhang; Liu, Yi; Zhang, Pengcheng; Yan, Rongbiao; Wang, Lei; Liu, Wenting; Gui, Zhiguo

doi:10.1016/j.ndteint.2022.102755

Cited by 8 publications

(1 citation statement)

References 24 publications

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“…Although the TV regularization method has achieved good reconstruction performance, certain limitations still exist [13,14]. In the past decade, various variants of TV regularization terms have been proposed, such as multi-directional TV [15], gradient directional TV [16], weighted TV [17,18], relative TV [19,20], and others [21][22][23][24][25][26][27][28]. One of the drawbacks of TV regularization is the potential occurrence of undesired patchy artifacts in reconstructed images, which can hinder the preservation of smooth transition structures.…”

Section: Introductionmentioning

confidence: 99%

A non-local total generalized variation regularization reconstruction method for sparse-view x-ray CT

Min,

Tao,

Liu

et al. 2024

Meas. Sci. Technol.

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Sparse-view X-ray computed tomography (CT) reconstruction, employing total generalised variation (TGV), effectively mitigates the stepwise artefacts associated with total variation (TV) regularisation while preserving structural features within transitional regions of the reconstructed image. Despite TGV surpassing TV in reconstruction quality, it neglects the non-local self-similarity prior, recognised for its efficacy in restoring details during CT reconstruction. This study introduces a non-local total generalised variation (NLTGV) to address the limitation of TGV regularisation method. Specifically, we propose an NLTGV-regularised method for sparse-view CT reconstruction, utilising non-local high-order derivative information to maintain image features and non-local self-similarity for detail recovery. Owing to the non-differentiability of the NLTGV regulariser, we employ an alternating direction method of multipliers (ADMM) optimisation method, facilitating an efficient solution by decomposing the reconstruction model into sub-problems. The proposed method undergoes evaluation using both simulated and real-world projection data. Simulation and experimental results demonstrate the efficacy of the proposed approach in enhancing the quality of reconstructed images compared to other competitive variational reconstruction methods. In conclusion, the simultaneous incorporation of sparsity priors of high-order TV and non-local similarity proves advantageous for structural detail recovery in sparse-view CT reconstruction.

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