Radiation Dose and Image Quality of Low-dose Protocol in Chest CT: Comparison of Standard-dose Protocol

Lee, Won-Jeong; Ahn, Bong-Seon; young-Sun, Park

doi:10.14407/jrp.2012.37.2.084

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Additionally, an equivalent ROI was applied to an area 3 cm from the outer-central section of the ART phantom, which was used to measure the background signal intensity and standard deviation. These values were substituted into Equation 1to obtain the signal-to-noise ratio (SNR) and contrast-to -noise ratio (CNR) (equation 2) (figure 2) (6)(7) .…”

Section: Image Evaluationmentioning

confidence: 99%

Changing the entrance surface dose and image quality by applying an air mattress to the table commonly used in computed tomography

Park

Kim²

2022

Int J Radiat Res

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Background: In this study, the changes in the entrance surface dose (ESD) and image quality were examined when an air mattress was used to reduce the X-ray scattering caused by treatment tables commonly used for computed tomography (CT). Materials and Methods: Dual-energy CT (DECT) was used, with the ESD of Alderson Radiation Therapy (ART) phantom measured by scanning with different X-ray tube voltages and X -ray tube currents for three scenarios: when no air mattress was employed, and when a 5 cm or 10 cm thick air mattress was employed. The statistical significance of the changes in ESD and image quality were based on the presence and thickness of the air mattress. Additionally, the variations of the X-ray tube voltage and current for different air mattress thicknesses were investigated using paired t-test. Results: For all X-ray tube voltages and currents, applying an air mattress significantly improved both the ESD and image quality, with the 5 cm air mattress improving both the ESD and image quality. Conclusion: The 5 cm air mattress produced no artifacts in the diagnostic images, and demonstrated a statistically significant reduction in patient ESD during DECT imaging.

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Section: Image Evaluationmentioning

confidence: 99%

Changing the entrance surface dose and image quality by applying an air mattress to the table commonly used in computed tomography

Park

Kim²

2022

Int J Radiat Res

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“…Although computed tomography (CT) technology has developed and achieved outstanding diagnostic accuracy, there remains concerns regarding radiation-induced cancers driving an as low as reasonably achievable (ALARA) movement [1][2][3]. The adoption of a low-dose CT (LDCT) protocol and the scan range optimization are exemplary efforts to lower imaging doses [4][5][6][7][8][9][10]. Particularly, LDCT has been popularly used in lung cancer screening program enabling to detect cancers in early stage, thereby reducing mortality rates [11,12].…”

Section: Introductionmentioning

confidence: 99%

Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program

et al. 2022

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We propose a deep learning-assisted overscan decision algorithm in chest low-dose computed tomography (LDCT) applicable to the lung cancer screening. The algorithm reflects the radiologists’ subjective evaluation criteria according to the Korea institute for accreditation of medical imaging (KIAMI) guidelines, where it judges whether a scan range is beyond landmarks’ criterion. The algorithm consists of three stages: deep learning-based landmark segmentation, rule-based logical operations, and overscan determination. A total of 210 cases from a single institution (internal data) and 50 cases from 47 institutions (external data) were utilized for performance evaluation. Area under the receiver operating characteristic (AUROC), accuracy, sensitivity, specificity, and Cohen’s kappa were used as evaluation metrics. Fisher’s exact test was performed to present statistical significance for the overscan detectability, and univariate logistic regression analyses were performed for validation. Furthermore, an excessive effective dose was estimated by employing the amount of overscan and the absorbed dose to effective dose conversion factor. The algorithm presented AUROC values of 0.976 (95% confidence interval [CI]: 0.925–0.987) and 0.997 (95% CI: 0.800–0.999) for internal and external dataset, respectively. All metrics showed average performance scores greater than 90% in each evaluation dataset. The AI-assisted overscan decision and the radiologist’s manual evaluation showed a statistically significance showing a p-value less than 0.001 in Fisher’s exact test. In the logistic regression analysis, demographics (age and sex), data source, CT vendor, and slice thickness showed no statistical significance on the algorithm (each p-value > 0.05). Furthermore, the estimated excessive effective doses were 0.02 ± 0.01 mSv and 0.03 ± 0.05 mSv for each dataset, not a concern within slight deviations from an acceptable scan range. We hope that our proposed overscan decision algorithm enables the retrospective scan range monitoring in LDCT for lung cancer screening program, and follows an as low as reasonably achievable (ALARA) principle.

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“…As the quality of the image deteriorates, the diagnostic success rates associated with using these images may also decrease. The Gaussian noise that is generated by scanning the brain at low radiation doses interferes with the accurate interpretation of these images and with the identification of brain-blood vessels as well as of cerebral parenchyma [14]. Therefore, the use of higher radiation doses appears to be unavoidable in order to improve the quality of images.…”

Section: Introductionmentioning

confidence: 99%

A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation

Yang

Hong

Kim

et al. 2020

Sensors

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Computed tomography (CT) is a widely used medical imaging modality for diagnosing various diseases. Among CT techniques, 4-dimensional CT perfusion (4D-CTP) of the brain is established in most centers for diagnosing strokes and is considered the gold standard for hyperacute stroke diagnosis. However, because the detrimental effects of high radiation doses from 4D-CTP may cause serious health risks in stroke survivors, our research team aimed to introduce a novel image-processing technique. Our singular value decomposition (SVD)-based image-processing technique can improve image quality, first, by separating several image components using SVD and, second, by reconstructing signal component images to remove noise, thereby improving image quality. For the demonstration in this study, 20 4D-CTP dynamic images of suspected acute stroke patients were collected. Both the images that were and were not processed via the proposed method were compared. Each acquired image was objectively evaluated using contrast-to-noise and signal-to-noise ratios. The scores of the parameters assessed for the qualitative evaluation of image quality improved to an excellent rating (p < 0.05). Therefore, our SVD-based image-denoising technique improved the diagnostic value of images by improving their quality. The denoising technique and statistical evaluation can be utilized in various clinical applications to provide advanced medical services.

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Radiation Dose and Image Quality of Low-dose Protocol in Chest CT: Comparison of Standard-dose Protocol

Cited by 5 publications

References 16 publications

Changing the entrance surface dose and image quality by applying an air mattress to the table commonly used in computed tomography

Changing the entrance surface dose and image quality by applying an air mattress to the table commonly used in computed tomography

Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program

A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation

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