Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization

Zhang, Houjin; Zeng, Dong; Lin, Jiahui; Zhang, Hao; Bian, Zhaoying; Huang, Jing; Gao, Yuanyuan; Zhang, Shanli; Zhang, Hua; Feng, Qianjin; Zhang, Liang; Chen, Wufan; Ma, Jianhua

doi:10.1088/1361-6560/aa7122

Cited by 41 publications

(24 citation statements)

References 41 publications

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“…3(C)). As comparisons, we also implement the image-based method, empirical dual energy calibration (EDEC) method (Stenner et al, 2007), nonlocal means filter based (NLM) method (Zeng et al, 2016b), weighted least square and nonlocal means based (WLS-NLM) method (Zhang et al, 2017), material decomposition from inconsistent rays (MDIR) (Maaβ et al, 2009b), and E-SART method (Zhao et al, 2015, Hu et al, 2016), covering all the decomposition categories and including both conventional and state-of-the-art methods. The corresponding descriptions and comparisons are summarized in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…However, the combination of polychromatic projections requires satisfying a geometrical consistency. Several iterative methods are proposed based on statistical models and nonlinear optimizations (Elbakri and Fessler, 2002, Xu et al, 2009, Maaβ et al, 2009b, Niu et al, 2014, Zeng et al, 2016b, Zhang et al, 2017, Zhang et al, 2014). By introducing prior knowledge or establishing an approximate model, these methods improve the decomposed image quality effectively.…”

Section: Introductionmentioning

confidence: 99%

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Locally linear constraint based optimization model for material decomposition

Wang

Zhu

2017

Phys. Med. Biol.

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Dual spectral computed tomography (DSCT) has a superior material distinguishability than the conventional single spectral computed tomography (SSCT). However, the decomposition process is an illposed problem, which is sensitive to noise. Thus, the decomposed image quality is degraded, and the corresponding signal-to-noise ratio (SNR) is much lower than that of directly reconstructed image of SSCT. In this work, we establish a locally linear relationship between the decomposed results of DSCT and SSCT. Based on this constraint, we propose an optimization model for DSCT and develop an iterative method with image guided filtering. To further improve the image quality, we employ a preprocessing method based on the relative total variation (RTV) regularization. Both numerical simulations and real experiments are performed, and the results confirm the effectiveness of our proposed approach.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Locally linear constraint based optimization model for material decomposition

Wang

Zhu

2017

Phys. Med. Biol.

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“…In addition to reconstruction methods a novel scheme based on measurement of DECT data has been advanced in order to attain clinically satisfactory DECT images from low-mAs acquisitions. In this scheme, enthused by the success of edge-preserving nonlocal means (NLM) filtering in CT imaging and the inherent features underlying DECT images, i.e., global correlation and nonlocal similarity, an averaged image induced NLM-based (aviNLM) regularization is combined into the penalized weighted least-squares (PWLS) framework [22]. More prominently, it provides the best qualitative outcomes with the premium details and the fewest noise-induced artifacts, due to the aviNLM regularization learned from DECT images [22].…”

Section: Image Reconstructionmentioning

confidence: 99%

Image Quality and Dose Comparison of Single-Energy CT (SECT) and Dual-Energy CT (DECT)

Shayan

Oladghaffari

Sajjadian

et al. 2020

Radiology Research and Practice

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CT and its comprehensive usage have become one of the most indispensable components in medical field especially in the diagnosis of several diseases. SECT and DECT have developed CT diagnostic potentials in several means. In this review article we have discussed the basic principles of single-energy and dual-energy computed tomography and their important physical differences which can cause better diagnostic evaluation. Moreover, different organs diagnostic evaluations through single-energy and dual-energy computed tomography have been discussed. Conventional or single-energy CT (SECT) uses a single polychromatic X-ray beam (ranging from 70 to 140 kVp with a standard of 120 kVp) emitted from a single source and received by a single detector. The concept of dual-energy computed tomography (DECT) is almost as old as the CT technology itself; DECT initially required substantially higher radiation doses (nearly two times higher than those employed in single-energy CT) and presented problems associated with spatial misregistration of the two different kV image datasets between the two separate acquisitions. The basic principles of single-energy and dual-energy computed tomography and their important physical differences can cause better diagnostic evaluation. Moreover, different organs diagnostic evaluations through single-energy and dual-energy computed tomography have been discussed. According to diverse data and statistics it is controversial to definitely indicate the accurate comparison of image quality and dose amount.

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“…By assuming that X-ray photons follow a Poisson distribution on reaching an X-ray detector, the MAP model can be reduced to a penalized weighted least squares (PWLS) optimization, including a term relating to the prior distribution of the target images [17], [20]. Owing to the difficulty in calculating the prior distribution, alternative priors reflecting the spatial correlation among neighboring pixels, such as total variation (TV) [6], [23], fractional-order TV [25], nonlocal TV [18], Markov random fields theory [17], [20], [21], or nonlocal means (NLM) [3], [10], [12], [26], have been imposed onto the target image. Although the IR method improves the overall image quality, some issues remain to be solved.…”

Section: Introductionmentioning

confidence: 99%

Low-Dose CT Image Reconstruction With a Deep Learning Prior

2020

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In low-dose computed tomography (LDCT), a penalized weighted least squares (PWLS) approach that incorporates the Poisson statistics of X-ray photons can significantly reduce excessive quantum noise. To improve the quality of LDCT images, prior information such as the total variation, Markov random field, and nonlocal mean, can be imposed onto the target image. However, this information may be limited to reflect the characteristics of the target images, thereby resulting in unexpected side effects (e.g. blurry images). In this paper, we propose a PWLS method combined with a deep learning prior, which is learned from standard dose CT (SDCT) images. The proposed model learns a noise reduction function that maps an LDCT image to its corresponding SDCT images and estimates the prior distribution using a Pearson χ 2 divergence. The model can be converted to the least squares generative adversarial network with an added PWLS objective, where the optimal generator acts as the noise reduction function. We reformulate the proposed model as a constrained optimization problem and solve it using the alternating optimization (AO) algorithm. Clinical SDCT and simulated LDCT scans of ten patients were used to show the validity of the proposed method. Results show that the proposed method outperforms other PWLS methods, by imposing priors such as the total variation and the nonlocal mean. INDEX TERMS Low-dose computed tomography, penalized weighted least squares, deep learning, denoising Recently, Park et al [19] have proposed an image-based deep learning model that can learn a noise reduction function that maps LDCT images to their standard dose CT (SDCT)

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Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization

Cited by 41 publications

References 41 publications

Locally linear constraint based optimization model for material decomposition

Locally linear constraint based optimization model for material decomposition

Image Quality and Dose Comparison of Single-Energy CT (SECT) and Dual-Energy CT (DECT)

Low-Dose CT Image Reconstruction With a Deep Learning Prior

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