Assessment of prior image induced nonlocal means regularization for low‐dose <scp>CT</scp> reconstruction: Change in anatomy

Zhang, Hao; Ma, Jianhua; Wang, Jing; Moore, William; Zhang, Liang

doi:10.1002/mp.12378

Cited by 15 publications

(17 citation statements)

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“…In clinical applications where a high‐quality prior image of the same patient exists, a family of priorHQ‐induced NLM regularizations for low‐dose CT reconstruction has also been studied and described as:

\begin{matrix} U (μ) & = \underset{j}{false\sum} \underset{k \in S W_{j}}{false\sum} w_{jk} false(boldμ, {boldμ}^{p r i o r H Q r e g i s t e r e d} false) \\ ϕ (μ_{j} - μ_{k}^{p r i o r H Q r e g i s t e r e d}) \end{matrix}

U false(boldμ false) = \underset{j}{false\sum} ϕ ()(, μ_{j} - \sum_{k \in S W_{j}} w_{jk} false(boldμ, {boldμ}^{p r i o r H Q r e g i s t e r e d} false) μ_{k}^{p r i o r H Q r e g i s t e r e d})

\begin{matrix} U false(boldμ false) = \frac{1}{p} \underset{j}{false\sum} ()(, \begin{matrix} \sum_{k \in S W_{j}} w_{italicjk} (μ, {boldμ}^{p r i o r H Q r e g i s t e r e d}) \\ (μ_{j} - μ_{k}^{p r i o r H Q r e g i s} \end{matrix}) \end{matrix}

…”

Section: Regularization Strategiesmentioning

confidence: 99%

Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review

et al. 2018

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Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.

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\begin{matrix} U (μ) & = \underset{j}{false\sum} \underset{k \in S W_{j}}{false\sum} w_{jk} false(boldμ, {boldμ}^{p r i o r H Q r e g i s t e r e d} false) \\ ϕ (μ_{j} - μ_{k}^{p r i o r H Q r e g i s t e r e d}) \end{matrix}

U false(boldμ false) = \underset{j}{false\sum} ϕ ()(, μ_{j} - \sum_{k \in S W_{j}} w_{jk} false(boldμ, {boldμ}^{p r i o r H Q r e g i s t e r e d} false) μ_{k}^{p r i o r H Q r e g i s t e r e d})

\begin{matrix} U false(boldμ false) = \frac{1}{p} \underset{j}{false\sum} ()(, \begin{matrix} \sum_{k \in S W_{j}} w_{italicjk} (μ, {boldμ}^{p r i o r H Q r e g i s t e r e d}) \\ (μ_{j} - μ_{k}^{p r i o r H Q r e g i s} \end{matrix}) \end{matrix}

…”

Section: Regularization Strategiesmentioning

confidence: 99%

Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review

et al. 2018

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“…Hence, on one hand, the tensor decomposition (TD) is used to represent a highly redundant tensor block to encourage the local sparsity and correlation. On the other hand, the non-local means (NLM) explores selfsimilarity of an image [30][31][32][33][34]. For a specific block, the information of similar blocks collected in a neighboring window can be used for denoising.…”

Section: Introductionmentioning

confidence: 99%

Tensor decomposition and non-local means based spectral CT image denoising

Zhang

Salehjahromi

2019

XST

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BACKGROUND: As one type of the state-of-the-art detectors, photon counting detectors are used in spectral computed tomography (CT) to classify the received photons into several energy channels and generate multichannel projections simultaneously. However, FBP reconstructed images contain severe noise due to the low photon counts in each energy channel. OBJECTIVE: A spectral CT image denoising method based on tensor-decomposition and nonlocal means (TDNLM) is proposed. METHODS: In a CT image, it is widely accepted that there exists self-similarity over the spatial domain. In addition, because a multichannel CT image is obtained from the same object at different energies, images among different channels are highly correlated. Motivated by these two characteristics of the spectral CT images, tensor decomposition and non-local means are employed to recover fine structures in spectral CT images. Moreover, images in all energy channels are added together to form a high signal-to-noise ratio image, which is applied to encourage the signal preservation of the TDNLM. The combination of TD, NLM and the guidance of a high-quality image enhances the low-dose spectral CT, and a parameter selection strategy is designed to achieve the optimal image quality. RESULTS: The effectiveness of the developed algorithm is validated on both numerical simulations and realistic preclinical applications. The root mean square error (RMSE) and the structural similarity (SSIM) are used to quantitatively assess the image quality. The proposed method successfully restored high-quality images (average RMSE=0.0217 cm −1 and SSIM=0.987) from noisy spectral CT images (average RMSE=0.225 cm −1 and SSIM=0.633). In addition, RMSE of each decomposed material component is also remarkably reduced. Compared to the state-of-the-art iterative spectral CT reconstruction algorithms, the proposed method achieves comparable performance with dramatically reduced computational cost, resulting in a speedup of >50. CONCLUSIONS: The outstanding denoising performance, the high computational efficiency and the adaptive parameter selection strategy make the proposed method practical for spectral CT applications.

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“…Stayman et al proposed a prior image registration penalized-likelihood estimation (PIRPLE) method by performing a joint registration of the prior image and a reconstruction of the low-dose data (Stayman et al 2013). Zhang et al proposed a prior image induced nonlocal (PINL) prior to exploit the nonlocal similarity between the current LdCT image and previous NdCT image (Zhang et al 2014b, Zhang et al 2017a). These priors can characterize the local structures of the desired image via learning NdCT information, but registration between the NdCT and LdCT images is usually needed, which is the major limitation for these methods.…”

Section: Introductionmentioning

confidence: 99%

Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image

et al. 2018

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In some clinical applications, prior normal-dose CT (NdCT) images are available, and the valuable textures and structure features in them may be used to promote follow-up low-dose CT (LdCT) reconstruction. This study aims to learn texture information from the NdCT images and leverage it for follow-up LdCT image reconstruction to preserve textures and structure features. Specifically, the proposed reconstruction method first learns the texture information from those patches with similar structures in NdCT image, and the similar patches can be clustered by searching context features efficiently from the surroundings of the current patch. Then it utilizes redundant texture information from the similar patches as a priori knowledge to describe specific regions in the LdCT image. The advanced region-aware texture preserving prior is termed as ‘RATP’. The main advantage of the PATP prior is that it can properly learn the texture features from available NdCT images and adaptively characterize the region-specific structures in the LdCT image. The experiments using patient data were performed to evaluate the performance of the proposed method. The proposed RATP method demonstrated superior performance in LdCT imaging compared to the filtered back projection (FBP) and statistical iterative reconstruction (SIR) methods using Gaussian regularization, Huber regularization and the original texture preserving regularization.

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Assessment of prior image induced nonlocal means regularization for low‐dose CT reconstruction: Change in anatomy

Cited by 15 publications

References 51 publications

Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review

Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT: A review

Tensor decomposition and non-local means based spectral CT image denoising

Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image

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