Iterative image‐domain decomposition for dual‐energy CT

Niu, Tianye; Dong, Xue; Petrongolo, Michael; Zhu, Lei

doi:10.1118/1.4866386

Cited by 127 publications

(172 citation statements)

References 64 publications

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“…In this paper, we improve a previously developed noise suppression method, PWLS-EPR, 11 for DECT decomposition by designing a new regularization term. PWLS-EPR includes gradient calculation in the regularization for edge preservation, and therefore fails to preserve the NPS of the original image after noise suppression.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…11 In image-domain material decomposition, it is assumed that the linear attenuation coefficient is approximated by a linear combination of two basis functions. The formulation of material decomposition is as follows:…”

Section: A Iterative Image-domain Decomposition With Noise Suppresmentioning

confidence: 99%

“…However, material differentiation via DECT still suffers from noise amplification during the signal decomposition process. [11][12][13] In this paper, we propose a noise suppression method for DECT, which is based on the statistical properties of decomposed images as well as the redundant structural information of the two CT images at different energy levels.…”

Section: Introductionmentioning

confidence: 99%

“…11,19,20 Decomposition in the projection domain has the advantage of being able to correct for beam-hardening artifacts. However this requires projection data which may not be available from a clinical CT scanner as many clinical scanners generate only the reconstructed CT images.…”

Section: Introductionmentioning

confidence: 99%

“…26 We recently proposed a new noise suppression algorithm for DECT, which is based on both redundant structural information and the statistical nature of the decomposition process. 11 We use a penalized weighted least-square optimization with the inverse of the variance-covariance matrix of the decomposed images as the penalty weight, and thus the decomposition becomes an iterative process. The method is further controlled by a regularization term, which aims to preserve edges of the initial CT images on the decomposed material images.…”

Section: Introductionmentioning

confidence: 99%

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Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

et al. 2016

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Purpose: Dual-energy CT (DECT) expands applications of CT imaging in its capability to decompose CT images into material images. However, decomposition via direct matrix inversion leads to large noise amplification and limits quantitative use of DECT. Their group has previously developed a noise suppression algorithm via penalized weighted least-square optimization with edge-preservation regularization (PWLS-EPR). In this paper, the authors improve method performance using the same framework of penalized weighted least-square optimization but with similarity-based regularization (PWLS-SBR), which substantially enhances the quality of decomposed images by retaining a more uniform noise power spectrum (NPS). Methods: The design of PWLS-SBR is based on the fact that averaging pixels of similar materials gives a low-noise image. For each pixel, the authors calculate the similarity to other pixels in its neighborhood by comparing CT values. Using an empirical Gaussian model, the authors assign high/low similarity value to one neighboring pixel if its CT value is close/far to the CT value of the pixel of interest. These similarity values are organized in matrix form, such that multiplication of the similarity matrix to the image vector reduces image noise. The similarity matrices are calculated on both high-and low-energy CT images and averaged. In PWLS-SBR, the authors include a regularization term to minimize the L-2 norm of the difference between the images without and with noise suppression via similarity matrix multiplication. By using all pixel information of the initial CT images rather than just those lying on or near edges, PWLS-SBR is superior to the previously developed PWLS-EPR, as supported by comparison studies on phantoms and a head-and-neck patient. Results: On the line-pair slice of the Catphan C 600 phantom, PWLS-SBR outperforms PWLS-EPR and retains spatial resolution of 8 lp/cm, comparable to the original CT images, even at 90% reduction in noise standard deviation (STD). Similar performance on spatial resolution is observed on an anthropomorphic head phantom. In addition, results of PWLS-SBR show substantially improved image quality due to preservation of image NPS. On the Catphan C 600 phantom, NPS using PWLS-SBR has a correlation of 93% with that via direct matrix inversion, while the correlation drops to −52% for PWLS-EPR. Electron density measurement studies indicate high accuracy of PWLS-SBR. On seven different materials, the measured electron densities calculated from the decomposed material images using PWLS-SBR have a root-mean-square error (RMSE) of 1.20%, while the results of PWLS-EPR have a RMSE of 2.21%. In the study on a head-and-neck patient, PWLS-SBR is shown to reduce noise STD by a factor of 3 on material images with image qualities comparable to CT images, whereas fine structures are lost in the PWLS-EPR result. Additionally, PWLS-SBR better preserves low contrast on the tissue image. Conclusions: The authors propose improvements to the regularization term of an optimization frame...

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: A Iterative Image-domain Decomposition With Noise Suppresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

et al. 2016

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Noise reduction in dual‐energy computed tomography virtual monoenergetic imaging

Liu

Huang

2019

J Applied Clin Med Phys

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Purpose Virtual monoenergetic images (VMIs) derived from dual‐energy computed tomography (DECT) have been explored for several clinical applications in recent years. However, VMIs at low and high keVs have high levels of noise. The aim of this study was to reduce image noise in VMIs by using a two‐step noise reduction technique. Methods VMI was first denoised using a modified highly constrained backprojection (HYPR) method. After the first‐step denoising, a general‐threshold filtering method was performed. Two sets of anthropomorphic phantoms were scanned with a clinical dual‐source DECT system. DECT data (80/140Sn kV) were reconstructed as VMI series at 12 different energy levels (range, 40‐150 keV, interval, 10 keV). For comparison, the averaged VMIs obtained from 10 repeated DECT scans were used as the reference standard. The signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR) and root‐mean‐square error (RMSE) were used to evaluate the quality of VMIs. Results Compared to the original HYPR method, the proposed two‐step image denoising method could provide better performance in terms of SNR, CNR, and RMSE. In addition, the proposed method could achieve effective noise reduction while preserving edges and small structures, especially for low‐keV VMIs. Conclusion The proposed two‐step image denoising method is a feasible method for reducing noise in VMIs obtained from a clinical DECT scanner. The proposed method can also reduce edge blurring and the loss of intensity in small lesions.

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Impact of iron deposit on the accuracy of quantifying liver fat fraction using multi‐material decomposition algorithm in dual‐energy spectral computed tomography

Jinchuan

et al. 2021

J Applied Clin Med Phys

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Iterative image‐domain decomposition for dual‐energy CT

Cited by 127 publications

References 64 publications

Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

Noise reduction in dual‐energy computed tomography virtual monoenergetic imaging

Impact of iron deposit on the accuracy of quantifying liver fat fraction using multi‐material decomposition algorithm in dual‐energy spectral computed tomography

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