Modeling and Pre-Treatment of Photon-Starved CT Data for Iterative Reconstruction

Chang, Zhiqian; Zhang, Ruoqiao; Thibault, Jean‐Baptiste; Pal, Debashish; Fu, Lin; Sauer, Ken D.; Bouman, Charles A.

doi:10.1109/tmi.2016.2606338

Cited by 27 publications

(18 citation statements)

References 33 publications

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“…We clipped nonpositive measurements by a threshold δ = 0.01 to enforce the logarithmic transform, which is the same as that in Wang et al . More advanced correction methods for nonpositive values may be favorable, but is out of the scope of this work. The corresponding images reconstructed by FBP and SIR‐ndiNLM [using Fig.…”

Section: Experiments and Resultsmentioning

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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Section: Experiments and Resultsmentioning

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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“…Assuming both noise sources are independent and additive, we have a ‘Poisson+ Gaussian’ model [66]

y_{i} ~ Poisson false{α ŷ_{i} false} / α + Normal false{0, σ^{2} false},

where y i and ŷ i are both in units of current-integrating DAS output, α is a scaling factor that converts the DAS output values to equivalent numbers of x-ray photons depending on the effective x-ray energy and the DAS gain, and σ 2 is the variance of the DAS electronic noise. σ 2 in modern CT DAS is typically equivalent to the counting statistics corresponding to from a handful [77][17][17][67][78] to a few hundred x-ray photons [65][37][13]. The parameters α and σ 2 can be measured by standard detector calibration processes [6].…”

Section: Statistical Modelsmentioning

confidence: 99%

“…In practice, smoothed or de-noised data can be used instead [38][31][36][32]. As an example, we implement a locally adaptive linear minimum mean squared-error (LLMMSE) filter very similar to what proposed by Chang, Zhang, Thibault, et al [78], where denoised data ӯ i , p̄ i and W̄ i , are used instead of the noisy data y i , p i , and W i .

ӯ_{i} = max false(denoise false(y_{i} false), δ false) .

{\bar{p}}_{i} = f_{i}^{- 1} true(log \frac{I_{i}}{ӯ_{i}} true) .

{\bar{W}}_{i} = f_{i}^{'} {false({\bar{p}}_{i} false)}^{2} \frac{ӯ_{i}^{2}}{ӯ_{i} + σ_{α}^{2}} .

The smoothing radius of the LLMMSE filter is typically 3×3 or 6× 6 on a 2D detector.…”

Section: Statistical Modelsmentioning

confidence: 99%

Comparison Between Pre-Log and Post-Log Statistical Models in Ultra-Low-Dose CT Reconstruction

Lee

Kim

et al. 2017

IEEE Trans. Med. Imaging

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X-ray detectors in clinical computed tomography (CT) usually operate in current-integrating mode. Their complicated signal statistics often lead to intractable likelihood functions for practical use in model-based image reconstruction (MBIR). It is therefore desirable to design simplified statistical models without losing the essential factors. Depending on whether the CT transmission data are logarithmically transformed, pre-log and post-log models are two major categories of choices in CT MBIR. Both being approximations, it remains an open question whether one model can notably improve image quality over the other on real scanners. In this study, we develop and compare several pre-log and post-log MBIR algorithms under a unified framework. Their reconstruction accuracy based on simulation and clinical datasets are evaluated. The results show that pre-log MBIR can achieve notably better quantitative accuracy than post-log MBIR in ultra-low-dose CT, although in less extreme cases, post-log MBIR with handcrafted pre-processing remains a competitive alternative. Pre-log MBIR could play a growing role in emerging ultra-low-dose CT applications.

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“…First, the logarithm transform may lead the projection measurements become zero or negative due to electronic noise 53 . Several methods 54–56 were analyzed for dealing with nonpositive values. Second, estimating proper weight parameters in WLS methods is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

CT image reconstruction algorithms: A comprehensive survey

Zhang

Xia

2019

Concurrency and Computation

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Summary With the application of computed tomography (CT) technology to the clinic diagnosis and image‐guided surgeries, various CT image reconstruction algorithms were presented for enhancing CT image quality based on different dose acquisitions. For wide and effective uses, this paper is going to provide a comprehensive survey on representative CT image reconstruction algorithms, in the light of low‐dose and high‐dose acquisitions and algorithm performance, including their merits and limitations. The algorithm performance and complexity are discussed under Gaussian and Poisson noise environments, respectively. Finally, experimental results analyze the efficiency and performance of several popular CT image reconstruction algorithms in terms of computation time and accuracy.

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Modeling and Pre-Treatment of Photon-Starved CT Data for Iterative Reconstruction

Cited by 27 publications

References 33 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

Comparison Between Pre-Log and Post-Log Statistical Models in Ultra-Low-Dose CT Reconstruction

CT image reconstruction algorithms: A comprehensive survey

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