Improving image quality in dual energy CT by edge-enhancing diffusion denoising

Petropoulos, Andreas E.; Vlachopoulos, George; Skiadopoulos, Spyros; Karahaliou, Anna; Costaridou, Lena

doi:10.1109/bibe.2013.6701597

Cited by 3 publications

(4 citation statements)

References 6 publications

(8 reference statements)

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“…The MAP framework performs denoising via voxel-wise operations and uses a prior on the quantitative parameters being fitted. The majority of denoising methods in DECT [19][20][21][22][23]32,33 perform noise reduction by using spatial priors, that is, aiming at reducing spatial variations in HU or physical parameters between neighboring voxels. This is known to reduce the spatial resolution and degrade the noise power spectrum of resulting parametric maps 34 , while performing noise reduction via voxel-wise operations appears to conserve the spatial resolution.…”

Section: Discussionmentioning

confidence: 99%

“…To reduce this noise, various approaches have been proposed in the literature. [19][20][21][22][23] While noise reduction is typically observed visually and through an increased contrast-to-noise ratio, the quantitative accuracy of parametric maps can be difficult to preserve. Some methods are also performed in iterative reconstruction schemes, which requires access to raw data.…”

Section: Introductionmentioning

confidence: 99%

“…Applied to noisy DECT data, least squares methods lead to amplified noise in

ρ_{e}

and

Z_{med}

parametric maps. To reduce this noise, various approaches have been proposed in the literature 19‐23 . While noise reduction is typically observed visually and through an increased contrast‐to‐noise ratio, the quantitative accuracy of parametric maps can be difficult to preserve.…”

Section: Introductionmentioning

confidence: 99%

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Electron density and effective atomic number estimation in a maximum a posteriori framework for dual‐energy computed tomography

et al. 2020

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The stoichiometric calibration method for dual-energy CT (DECT) proposed by Bourque et al. (Phys Med Biol. 59:2059; 2014), which provides estimators of the electron density and the effective atomic number, is adapted to a maximum a posteriori (MAP) framework to increase the model's robustness to noise and biases in CT data, specifically for human tissues. Robust physical parameter estimation from noisy DECT scans is required to maximize the precision of quantities used for radiotherapy treatment planning such as the proton stopping power (SPR). Methods: Estimation of electron density and effective atomic number is performed by constraining their variation to the natural range of values expected for human tissues, while maximizing attenuation data fidelity. The MAP framework is first compared against the original method using theoretical CT numbers with Gaussian noise. The quantitative accuracy of the MAP framework is then validated experimentally on the Gammex 467 phantom. Then, using two clinical datasets, the advantages of the approach are experimentally evaluated, qualitatively, and quantitatively. Results: The theoretical study shows that the root-mean-square error on the electron density, the effective atomic number and the SPR are, respectively, reduced from 2.3 to 1.5, 5.7 to 3.2 and 2.8 to 1.7% with the adapted framework, when analyzing soft tissues and bone together. The experimental validation study shows that the standard deviation in Gammex inserts can be reduced, on average, by factors of 1.4 (electron density), 2.7 (effective atomic number), and 1.9 (SPR), while the quantitative accuracy of the three physical parameters is preserved, on average. Evaluation on clinical datasets show apparent noise reduction in maps of all estimated physical quantities, and suggests that the MAP framework has increased robustness to beam hardening and photon starvation artifacts. Mean values for the electron density, the effective atomic number, and the SPR averaged in four uniform regions of interest (brain, muscle, adipose, and cranium), respectively, differ by 0.7, 1.8, and 0.9% between both frameworks. The standard deviation in the same regions of interest is also reduced, on average, by factors of 1.8, 6.6, and 3.2 with the MAP framework. Differences in mean value and standard deviations are statistically significant. Conclusion: Theoretical and experimental results suggest that the MAP framework produces more accurate and precise estimates of the electron density and SPR. Thus, the present approach limits the propagation of noise in DECT attenuation data to radiotherapy-related parameters maps such as the SPR and the electron density. Using a MAP framework with DECT for radiotherapy treatment planning can help maximizing the precision of dose calculation. The method also provides more precise estimates of the effective atomic number. The MAP methodology is presented in a general way such that it can be adapted to any DECT image-based tissue characterization method.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Applied to noisy DECT data, least squares methods lead to amplified noise in

ρ_{e}

and

Z_{med}

Section: Introductionmentioning

confidence: 99%

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Electron density and effective atomic number estimation in a maximum a posteriori framework for dual‐energy computed tomography

et al. 2020

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“…The quality of the improved image is quantitatively evaluated by using the contrast-to-noise ratio (CNR) [12], [13]. Regions B and C in Fig.…”

Section: Image Quality Evaluation Using Simulated Imagesmentioning

confidence: 99%

Real-Time Color Image Improvement System for Visual Testing of Nuclear Reactors

Hosoya

Miyamoto

Naganuma

2018

IEICE Trans. Inf. & Syst.

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Nuclear power plants require in-vessel inspections for soundness checks and preventive maintenance. One inspection procedure is visual testing (VT), which is based on video images of an underwater camera in a nuclear reactor. However, a lot of noise is superimposed on VT images due to radiation exposure. We propose a technique for improving the quality of those images by image processing that reduces radiation noise and enhances signals. Real-time video processing was achieved by applying the proposed technique with a parallel processing unit. Improving the clarity of VT images will lead to reducing the burden on inspectors.

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