A Simple Low-Dose X-Ray CT Simulation From High-Dose Scan

Zeng, Dong; Huang, Jing; Bian, Zhaoying; Niu, Shanzhou; Zhang, Hua; Feng, Qianjin; Zhang, Liang; Ma, Jianhua

doi:10.1109/tns.2015.2467219

Cited by 127 publications

(65 citation statements)

References 26 publications

(48 reference statements)

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“…We simulated the corresponding low‐dose projection data by adding noise to the normal‐dose projection data using the simulation method described in Ref. . The noisy measurement

N_{i}

at detector bin i was generated according to the statistical model:

N_{i} \sim P o i s s o n \{N_{0} exp (- {\overset{y}{true}}_{i})\} + G a u s s i a n \{0, {italicσ}_{e}^{2}\}

where

N_{0}

was set to 3 × 10 4 and

σ_{e}^{2}

was set to 10 for low‐dose scan simulation.…”

Section: Methodsmentioning

confidence: 99%

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

Zhang

Wang

et al. 2017

Medical Physics

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Purpose Repeated computed tomography (CT) scans are prescribed for some clinical applications such as lung nodule surveillance. Several studies have demonstrated that incorporating a high-quality prior image into the reconstruction of subsequent low-dose CT (LDCT) acquisitions can either improve image quality or reduce data fidelity requirements. Our proposed previous normal-dose image induced nonlocal means (ndiNLM) regularization method for LDCT is an example of such a method. However, one major concern with prior image based methods is that they might produce false information when the prior image and the current LDCT image show different structures (for example, if a lung nodule emerges, grows, shrinks or disappears over time). This study aims to assess the performance of the ndiNLM regularization method in situations with change in anatomy. Method We incorporated the ndiNLM regularization into the statistical image reconstruction (SIR) framework for reconstruction of subsequent LDCT images. Because of its patch-based search mechanism, a rough registration between the prior image and the current LDCT image is adequate for the SIR-ndiNLM method. We assessed the performance of the SIR-ndiNLM method in lung nodule surveillance for two different scenarios: (1) the nodule was not found in a baseline exam but appears in a follow-up LDCT scan; (2) the nodule was present in a baseline exam but disappears in a follow-up LDCT scan. We further investigated the effect of nodule size on the performance of the SIR-ndiNLM method. Results We found that a relatively large search-window (e.g., 33×33) should be used for the SIR-ndiNLM method to account for misalignment between the prior image and the current LDCT image, and to ensure that enough similar patches can be found in the prior image. With proper selection of other parameters, experimental results with two patient datasets demonstrated that the SIR-ndiNLM method did not miss true nodules nor introduce false nodules in the lung nodule surveillance scenarios described above. We also found that the SIR-ndiNLM reconstruction shows improved image quality when the prior image is similar to the current LDCT image in anatomy. These gains in image quality might appear small upon visual inspection, but they can be detected using quantitative measures. Finally, the SIR-ndiNLM method also performed well in ultra-low-dose conditions and with different nodule sizes. Conclusions This study assessed the performance of the SIR-ndiNLM method in situations in which the prior image and the current LDCT image show substantial anatomical differences, specifically, changes in lung nodules. The experimental results demonstrate that the SIR-ndiNLM method does not introduce false lung nodules nor miss true nodules, which relieves the concern that this method might produce false information. However, there is insufficient evidence that these findings will hold true for all kinds of anatomical changes.

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“…We simulated the corresponding low‐dose projection data by adding noise to the normal‐dose projection data using the simulation method described in Ref. . The noisy measurement

N_{i}

at detector bin i was generated according to the statistical model:

N_{i} \sim P o i s s o n \{N_{0} exp (- {\overset{y}{true}}_{i})\} + G a u s s i a n \{0, {italicσ}_{e}^{2}\}

where

N_{0}

was set to 3 × 10 4 and

σ_{e}^{2}

was set to 10 for low‐dose scan simulation.…”

Section: Methodsmentioning

confidence: 99%

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

Zhang

Wang

et al. 2017

Medical Physics

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“…The XCAT phantom consists of left ventricle, aorta, healthy myocardium, ischemic myocardium, and right ventricle were represented by the TACs. A simulation method [7], [19], was used to acquire the low-dose myocardial perfusion CT sinogram (MPCT) data. When we have the noise-free line integral Y , the noisy measurement P i at each bin i was generated according to the statistical model of pre-logarithm projection data (i.e., Eq.…”

Section: Resultsmentioning

confidence: 99%

Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism

Xie

Zeng

Zhao

et al. 2017

IEEE Trans. Med. Imaging

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Computed tomography (CT) image recovery from low-mAs acquisitions without adequate treatment is always severely degraded due to a number of physical factors. In this paper, we formulate the low-dose CT sinogram preprocessing as a standard maximum a posteriori (MAP) estimation, which takes full consideration of the statistical properties of the two intrinsic noise sources in low-dose CT, i.e., the X-ray photon statistics and the electronic noise background. In addition, instead of using a general image prior as found in traditional sinogram recovery models, we design a new prior formulation to more rationally encode the piecewise-linear configurations underlying a sinogram than previously used ones, like the total variation prior term. As compared with the previous methods, especially the MAP-based ones, both the likelihood/loss and prior/regularization terms in the proposed model are ameliorated in a more accurate manner and better comply with the statistical essence of the generation mechanism of a practical sinogram. We further construct an efficient alternating direction method of multipliers (ADMM) algorithm to solve the proposed MAP solution. Experiments on simulated and real low-dose CT data demonstrate the superiority of the proposed method according to both visual inspection and comprehensive quantitative performance evaluation.

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“…Each projection datum along an X‐ray through the sectional image was calculated based on the known densities and intersection areas of the ray with the geometric shapes of the objects in the sectional image. For the sinogram data simulation, as in our previous studies, after calculating the noise‐free line integral

\overset{y}{true}

via direct projection, the noisy measurement

{\overset{false~}{I}}_{i}

at each bin i was generated according to the statistical model of the prelogarithmic projection data, as follows:

{\overset{I}{true}}_{i} = Poisson false(I_{0} exp (- {\overset{y}{true}}_{i}) false) + Normal false(0, σ_{e}^{2} false),

where I 0 denotes the incident X‐ray intensity, and

σ_{e}^{2}

is the background electronic noise variance. Then, the noisy measurement

{\overset{false~}{y}}_{i}

was calculated as the logarithmic transform of

{\overset{false~}{I}}_{i}

.…”

Section: Methodsmentioning

confidence: 99%

“…Then, the noisy measurement

{\overset{false~}{y}}_{i}

was calculated as the logarithmic transform of

{\overset{false~}{I}}_{i}

. In this work, the X‐ray exposure level I 0 was set to 1.0×10 5 , and

σ_{e}^{2}

was set to 10.0 for low‐dose simulations …”

Section: Methodsmentioning

confidence: 99%

Low‐dose dynamic myocardial perfusion CT imaging using a motion adaptive sparsity prior

et al. 2017

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Purpose: Dynamic myocardial perfusion computed tomography (DM-PCT) imaging offers benefits over quantitative assessment of myocardial blood flow (MBF) for diagnosis and risk stratification of coronary artery disease. However, one major drawback of DM-PCT imaging is that a high radiation level is imparted by repeated scanning. To address this issue, in this work, we developed a statistical iterative reconstruction algorithm based on the penalized weighted least-squares (PWLS) scheme by incorporating a motion adaptive sparsity prior (MASP) model to achieve high-quality DM-PCT imaging with low tube current dynamic data acquisition. For simplicity, we refer to the proposed algorithm as "PWLS-MASP''. Methods: The MASP models both the spatial and temporal structured sparsity of DM-PCT sequence images with the assumption that the differences between adjacent frames after motion correction are sparse in the gradient image domain. To validate and evaluate the effectiveness of the present PWLS-MASP algorithm thoroughly, a modified XCAT phantom and preclinical porcine DM-PCT dataset were used in the study.Results: The present PWLS-MASP algorithm can obtain high-quality DM-PCT images in both phantom and porcine cases, and outperforms the existing filtered back-projection algorithm and PWLS-based algorithms with total variation regularization (PWLS-TV) and robust principal component analysis regularization (PWLS-RPCA) in terms of noise reduction, streak artifacts mitigation, and time density curve estimation. Moreover, the PWLS-MASP algorithm can yield more accurate diagnostic hemodynamic parametric maps than the PWLS-TV and PWLS-RPCA algorithms. Conclusions: The study indicates that there is a substantial advantage in using the present PWLS-MASP algorithm for low-dose DM-PCT, and potentially in other dynamic tomography areas.

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A Simple Low-Dose X-Ray CT Simulation From High-Dose Scan

Cited by 127 publications

References 26 publications

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

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

Robust Low-Dose CT Sinogram Preprocessing via Exploiting Noise-Generating Mechanism

Low‐dose dynamic myocardial perfusion CT imaging using a motion adaptive sparsity prior

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