Noise reduction for low-dose single-slice Helical CT sinogram

Image noise is one of the main restrictions in computed tomography (CT) with respect to the trade-off between resolution and dose. An HD-MAF is proposed based on a local histogram function identifying the most representative CT value. The local histogram function is computed over an n-dimensional homogenous subvolume which is defined by a principal component analysis. Image filtering affects only predefined CT values which can be seen as image background. The filter method was applied to images of a low-contrast phantom, a low-dose head scan and a thorax scan. For the low-contrast phantom the contrast to noise ratio (CNR) was increased by a factor of 1.8. For the head images a noise reduction of 48% was achieved improving the overall image impression. The corresponding difference images showed no loss of structural information. The 4-dimensional HD-MAF provided images without motion artefacts in the pericardiac region and an overall reduced image noise. HD-MAF is a feasible method for low-contrast enhancement.Index Terms-CT, image filtering, contrast to noise ratio.

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Histogram-driven multi-dimensional adaptive filtering (HD-MAF)

Ertel

Kachelrieß

Kalender

2007

2007 IEEE Nuclear Science Symposium Conference Record

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Virtual colonoscopy screening with ultra low-dose CT: A simulation study

Wang

et al. 2007

2007 IEEE Nuclear Science Symposium Conference Record

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Computed tomography colonography (CTC) or CTbased virtual colonoscopy (VC) is an emerging tool for detection of colonic polyps. Compared to the conventional fiber-optic colonoscopy, VC has demonstrated the potential to become a mass screening modality in terms of safety, cost, and patient compliance. However, current CTC delivers excessive X-ray radiation to the patient during data acquisition. The radiation dose is a major concern for screening application of CTC. In this work, we performed a simulation study to demonstrate a possible ultra low-dose CT technique for VC. The ultra low-dose abdominal CT images were simulated by adding noise to the sinograms of the patient CTC images acquired with normal dose scans at 100 mAs levels. The simulated noisy sinogram or projection data were first processed by a Karhunen-Loève domain penalized weighted least squares (KL-PWLS) restoration method and then reconstructed by a filtered backprojection algorithm for the ultra low-dose CT images. The patient-specific virtual colon lumen was constructed and navigated by a VC system after electronic colon cleansing of the orally-tagged residue stool and fluid. By the KL-PWLS noise reduction, the colon lumen can be successfully constructed and the colonic polyp can be detected in an ultra low-dose level below 50 mAs. Polyp detection was also found easier by the KL-PWLS noise reduction compared to the results using the conventional noise filters, such as Hanning filter. These promising results indicate the feasibility of an ultra low-dose CTC pipeline for colon screening.

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Noise reduction for low-dose helical CT by 3D penalized weighted least-squares sinogram smoothing

Wang

et al. 2006

SPIE Proceedings

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Helical computed tomography (HCT) has several advantages over conventional step-and-shoot CT for imaging a relatively large object, especially for dynamic studies. However, HCT may increase X-ray exposure significantly to the patient. This work aims to reduce the radiation by lowering the X-ray tube current (mA) and filtering the low-mA (or dose) sinogram noise. Based on the noise properties of HCT sinogram, a three-dimensional (3D) penalized weighted least-squares (PWLS) objective function was constructed and an optimal sinogram was estimated by minimizing the objective function. To consider the difference of signal correlation among different direction of the HCT sinogram, an anisotropic Markov random filed (MRF) Gibbs function was designed as the penalty. The minimization of the objection function was performed by iterative Gauss-Seidel updating strategy. The effectiveness of the 3D-PWLS sinogram smoothing for low-dose HCT was demonstrated by a 3D Shepp-Logan head phantom study. Comparison studies with our previously developed KL domain PWLS sinogram smoothing algorithm indicate that the KL+2D-PWLS algorithm shows better performance on in-plane noise-resolution trade-off while the 3D-PLWS shows better performance on z-axis noise-resolution trade-off. Receiver operating characteristic (ROC) studies by using channelized Hotelling observer (CHO) shows that 3D-PWLS and KL+2DPWLS algorithms have similar performance on detectability in low-contrast environment.

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Noise reduction for low-dose single-slice Helical CT sinogram

Cited by 5 publications

References 24 publications

Histogram-driven multi-dimensional adaptive filtering (HD-MAF)

Histogram-driven multi-dimensional adaptive filtering (HD-MAF)

Virtual colonoscopy screening with ultra low-dose CT: A simulation study

Noise reduction for low-dose helical CT by 3D penalized weighted least-squares sinogram smoothing

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