Improved precision of noise estimation in CT with a volume-based approach

Wisselink, Hendrik Joost; Pelgrim, Gert Jan; Rook, Mieneke; Dudurych, I.M.; Berge, Maarten van den; Bock, Geertruida H. de; Vliegenthart, Rozemarijn

doi:10.1186/s41747-021-00237-x

Cited by 2 publications

(2 citation statements)

References 24 publications

(29 reference statements)

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“…It is further defined by detectors (quantum noise) depending on the number of photons received. The image noise decreases as the number of photons received increases [ 9 ]. In CT systems, the amount of noise is inversely related to resolution, with spatial resolution referring to its ability to display images of closely positioned objects as separate entities [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Investigating the slice thickness effect on noise and diagnostic content of single-source multi-slice computerized axial tomography

Abdulkareem,

Hajee,

Hassan

et al. 2023

JMedLife

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High-quality and detailed CT scan images are crucial for accurate diagnosis. Factors such as image noise and slice thickness affect image quality. This study aimed to determine the optimal slice thickness that minimized image noise while maintaining sufficient diagnostic information using the single-source computed tomography head protocol. Single-source CT images were examined using the Linux Operating system Ge Revolution 64-slice CT scanner, and a combination of statical analysis and DICOM CT image analysis was employed. The single-source energy head CT protocol was used to investigate the effect of slice thickness on noise and visibility in images. Different values of slice thickness 0.625, 1.25, 2.5, 3.75, 5, 7.5, and 10 were prepared, and then quantitative analysis was performed. Thinner slice thickness decreased image noise, increased visibility, and improved detection. Therefore, the balance between changing the thickness of the slice with the diagnostic content and image noise must be considered. Maximum slice thickness enhances CT image detail and structure despite more noise. Based on the results, a slice thickness of 1.25mm was identified as the optimal choice for reducing image noise and achieving better and more accurate detection using the single-source computed tomography head protocol. The study revealed that image noise tends to increase with greater slice thickness according to the Linux operating system. These findings can serve as a valuable guide for quality control methods in CT centers, emphasizing the need to determine the appropriate slice thickness to ensure an accurate diagnosis.

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Section: Introductionmentioning

confidence: 99%

Investigating the slice thickness effect on noise and diagnostic content of single-source multi-slice computerized axial tomography

Abdulkareem,

Hajee,

Hassan

et al. 2023

JMedLife

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“…However, the accuracy of CT number measurement might be disrupted by the presence of noise [ 11 ], defined as an undesirable fluctuation of CT numbers in images due to the imperfection of the CT system [ 12 - 14 ]. However, the presence of noise is evitable in the CT image [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of Noise Level on the Accuracy of Automated Measurement of CT Number Linearity on ACR CT and Computational Phantoms

2023

J Biomed Phys Eng

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Background: Methods for segmentation, i.e., Full-segmentation (FS) and Segmentation-rotation (SR), are proposed for maintaining Computed Tomography (CT) number linearity. However, their effectiveness has not yet been tested against noise. Objective: This study aimed to evaluate the influence of noise on the accuracy of CT number linearity of the FS and SR methods on American College of Radiology (ACR) CT and computational phantoms. Material and Methods: This experimental study utilized two phantoms, ACR CT and computational phantoms. An ACR CT phantom was scanned by a 128-slice CT scanner with various tube currents from 80 to 200 mA to acquire various noises, with other constant parameters. The computational phantom was added by different Gaussian noises between 20 and 120 Hounsfield Units (HU). The CT number linearity was measured by the FS and SR methods, and the accuracy of CT number linearity was computed on two phantoms. Results: The two methods successfully segmented both phantoms at low noise, i.e., less than 60 HU. However, segmentation and measurement of CT number linearity are not accurate on a computational phantom using the FS method for more than 60-HU noise. The SR method is still accurate up to 120 HU of noise. Conclusion: The SR method outperformed the FS method to measure the CT number linearity due to its endurance in extreme noise.

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Improved precision of noise estimation in CT with a volume-based approach

Cited by 2 publications

References 24 publications

Investigating the slice thickness effect on noise and diagnostic content of single-source multi-slice computerized axial tomography

Investigating the slice thickness effect on noise and diagnostic content of single-source multi-slice computerized axial tomography

Impact of Noise Level on the Accuracy of Automated Measurement of CT Number Linearity on ACR CT and Computational Phantoms

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