An experimental study on the noise properties of x-ray CT sinogram data in Radon space

Wang, Jing; Lu, Hongbing; Zhang, Liang; Eremina, D.; Zhang, Guangxiang; Wang, Su; Chen, John; Manzione, James V.

doi:10.1088/0031-9155/53/12/018

Cited by 154 publications

(147 citation statements)

References 12 publications

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“…20 Nevertheless, the normal distribution is widely used and has been experimentally validated. For example, Wang et al 21 used the Shapiro-Wilk test 22 to show that the distribution of prereconstruction CT sinogram data is described by a normal distribution with high confidence. The Shapiro-Wilk test is based on the normal probability plot.…”

Section: ͑25͒mentioning

confidence: 99%

Near optimal energy selective x‐ray imaging system performance with simple detectors

Alvarez¹

2010

Medical Physics

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Purpose: This article describes a method to achieve near optimal performance with low energy resolution detectors. Tapiovaara and Wagner ͓Phys. Med. Biol. 30, 519-529 ͑1985͔͒ showed that an energy selective x-ray system using a broad spectrum source can produce images with a larger signal to noise ratio ͑SNR͒ than conventional systems using energy integrating or photon counting detectors. They showed that there is an upper limit to the SNR and that it can be achieved by measuring full spectrum information and then using an optimal energy dependent weighting. Methods: A performance measure is derived by applying statistical detection theory to an abstract vector space of the line integrals of the basis set coefficients of the two function approximation to the x-ray attenuation coefficient. The approach produces optimal results that utilize all the available energy dependent data. The method can be used with any energy selective detector and is applied not only to detectors using pulse height analysis ͑PHA͒ but also to a detector that simultaneously measures the total photon number and integrated energy, as discussed by Roessl et al. ͓Med. Phys. 34, 959-966 ͑2007͔͒. A generalization of this detector that improves the performance is introduced. A method is described to compute images with the optimal SNR using projections in a "whitened" vector space transformed so the noise is uncorrelated and has unit variance in both coordinates. Material canceled images with optimal SNR can also be computed by projections in this space. Results:The performance measure is validated by showing that it provides the Tapiovaara-Wagner optimal results for a detector with full energy information and also a conventional detector. The performance with different types of detectors is compared to the ideal SNR as a function of x-ray tube voltage and subject thickness. A detector that combines two bin PHA with a simultaneous measurement of integrated photon energy provides near ideal performance across a wide range of operating conditions. Conclusions: Low energy resolution detectors can be used in energy selective x-ray imaging systems to produce images with near optimal performance.

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Section: ͑25͒mentioning

confidence: 99%

Near optimal energy selective x‐ray imaging system performance with simple detectors

Alvarez¹

2010

Medical Physics

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“…In the previous study, 16 the mean-variance relationship involves one parameter for each detector bin i, i.e., i , and so this parameter at each bin i was first calculated from the corresponding pair (p i , σ…”

Section: Resultsmentioning

confidence: 99%

“…(17) might be more meaningful than Eq. (16) in analyzing the relative importance of those different termsT i (or mean radiation intensity) for low-dose CT imaging. The first term in Eq.…”

Section: Iic Variance Analysis Of the Transmission Data In Ct Sinogmentioning

confidence: 99%

“…(16). Since λ indicates the mean radiation intensity, thenT 1 = 1/λ was used as the reference (i.e., the case of ignoring the electronic noise background).…”

Section: Iic Variance Analysis Of the Transmission Data In Ct Sinogmentioning

confidence: 99%

“…(20), according to the calibrated output of sinograms as described in Ref. 16, the outputted sinograms in this study were scaled by a scaling factor 2294.5, which relates to the number of bits stored in the computer. In other words, the stored digital number and the physical line integral are related bŷ p i = 2294.5p i .…”

Section: Iid Data Acquisitionmentioning

confidence: 99%

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Variance analysis of x-ray CT sinograms in the presence of electronic noise background

et al. 2012

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Purpose: Low-dose x-ray computed tomography (CT) is clinically desired. Accurate noise modeling is a fundamental issue for low-dose CT image reconstruction via statistics-based sinogram restoration or statistical iterative image reconstruction. In this paper, the authors analyzed the statistical moments of low-dose CT data in the presence of electronic noise background. Methods: The authors first studied the statistical moment properties of detected signals in CT transmission domain, where the noise of detected signals is considered as quanta fluctuation upon electronic noise background. Then the authors derived, via the Taylor expansion, a new formula for the mean-variance relationship of the detected signals in CT sinogram domain, wherein the image formation becomes a linear operation between the sinogram data and the unknown image, rather than a nonlinear operation in the CT transmission domain. To get insight into the derived new formula by experiments, an anthropomorphic torso phantom was scanned repeatedly by a commercial CT scanner at five different mAs levels from 100 down to 17. Results:The results demonstrated that the electronic noise background is significant when low-mAs (or low-dose) scan is performed. Conclusions:The influence of the electronic noise background should be considered in low-dose CT imaging.

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Applications of nonlocal means algorithm in low‐dose X‐ray CT image processing and reconstruction: A review

et al. 2017

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Low-dose X-ray computed tomography (LDCT) imaging is highly recommended for use in the clinic because of growing concerns over excessive radiation exposure. However, the CT images reconstructed by the conventional filtered back-projection (FBP) method from low-dose acquisitions may be severely degraded with noise and streak artifacts due to excessive X-ray quantum noise, or with view-aliasing artifacts due to insufficient angular sampling. In 2005, the nonlocal means (NLM) algorithm was introduced as a non-iterative edge-preserving filter to denoise natural images corrupted by additive Gaussian noise, and showed superior performance. It has since been adapted and applied to many other image types and various inverse problems. This paper specifically reviews the applications of the NLM algorithm in LDCT image processing and reconstruction, and explicitly demonstrates its improving effects on the reconstructed CT image quality from low-dose acquisitions. The effectiveness of these applications on LDCT and their relative performance are described in detail.

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An experimental study on the noise properties of x-ray CT sinogram data in Radon space

Cited by 154 publications

References 12 publications

Near optimal energy selective x‐ray imaging system performance with simple detectors

Near optimal energy selective x‐ray imaging system performance with simple detectors

Variance analysis of x-ray CT sinograms in the presence of electronic noise background

Applications of nonlocal means algorithm in low‐dose X‐ray CT image processing and reconstruction: A review

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