Automated Technique to Measure Noise in Clinical CT Examinations

Christianson, Olav; Winslow, J; Frush, Donald P.; Samei, Ehsan

doi:10.2214/ajr.14.13613

Cited by 100 publications

(143 citation statements)

References 17 publications

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“…Although such an analysis could be performed with phantoms, phantom data would only provide information concerning specific exposure parameters for a scan: instead, a general population comparison is a way to obtain new information related to the patient adaptability of different scanners. The analysis was possible by means of the DHDMP, which stores radiation dose information and estimates patient size and noise value data . It is possible to reproduce this analysis using patient water equivalent diameter instead that the patient effective diameter estimated by the DHDMP system.…”

Section: Discussionmentioning

confidence: 99%

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Image noise and dose performance across a clinical population: Patient size adaptation as a metric of CT performance

et al. 2017

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PURPOSE.Modern CT systems adjust x-ray flux accommodating for patient size to achieve certain image noise values. The effectiveness of this adaptation is an important aspect of CT performance and should ideally be characterized in the context of real patient cases. The objective of this study was to characterize CT performance with a new metric that includes image noise and radiation dose across a clinical patient population. MATERIALS AND METHODS.The study included 1526 examinations performed by three CT scanners (one GE Healthcare Discovery CT750HD, one GE Healthcare Lightspeed VCT, and one Siemens SOMATOM definition Flash) used for two routine clinical protocols (abdominopelvic with contrast and chest without contrast). An institutional monitoring system recorded all the data involved in the study. CONCLUSIONS.A theoretical relationship between image noise, CTDI vol and patient size was determined based on real patient data. This relationship may be interpreted as a new metric related to the scanners' adaptability concerning image quality and radiation dose across a patient population. This method could Accepted ArticleThis article is protected by copyright. All rights reserved.be implemented to investigate the adaptability related to other image quality indexes and radiation dose in a clinical population.

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Section: Discussionmentioning

confidence: 99%

“…The final noise value for the image set is computed as the mean of the noise values. A previous study describes in detail this analysis and the reliability of the noise values compared to a phantom and observer analysis. In particular, Christianson et al.…”

Section: Methodsmentioning

confidence: 99%

Image noise and dose performance across a clinical population: Patient size adaptation as a metric of CT performance

et al. 2017

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“…is the workflow which starts with an axial CT image stack and ends with a histogram of ROI standard deviation values for either an air or water region. In this paper, we refer to water segmentation, but in clinical practice, this is analogous to the soft tissue segmentation performed by others . The workflow begins with axial CT image data (yellow) and is composed of two main phases, which are carried out independently of each other.…”

Section: Methodsmentioning

confidence: 99%

“…The meanGNI air metric value for each case was plotted against the measured liver noise and fit with a linear trend line. The purpose of this comparison is to show that meanGNI air is proportional to actual image noise in order to validate our methods as previous groups in this field have done . Both meanGNI air and liver standard deviation values were normalized to a RFOV of 500 cm as described above.…”

Section: Methodsmentioning

confidence: 99%

A method to extract image noise level from patient images in CT

Malkus

Szczykutowicz

2017

Med. Phys.

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“…The three scanners were anonymized and they are identified as CT1, CT2 and CT3. All data presented in this work came from the Duke Health dose monitoring program (DHDMP) [8], [9].…”

Section: Methodsmentioning

confidence: 99%

Adaptability index: quantifying CT tube current modulation performance from dose and quality informatics

Ria

Wilson

Zhang

et al. 2017

Medical Imaging 2017: Physics of Medical Imaging

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The balance between risk and benefit in modern CT scanners is governed by the automatic adaptation mechanisms that adjust x-ray flux for accommodating patient size to achieve certain image noise values. The effectiveness of this adaptation is an important aspect of CT performance and should ideally be characterized in the context of real patient cases. Objective of this study was to characterize CT performance with an index that includes image-noise and radiationdose across a clinical patient population.The study included 1526 examinations performed by three scanners, from two vendors, used for two clinical protocols (abdominopelvic and chest). The dose-patient size and noise-patient size dependencies were linearized, and a 3D-fit was performed for each protocol and each scanner with a planar function. In the fit residual plots the Root Mean Square Error (RMSE) values were estimated as a metric of CT adaptability across the patient population.The RMSE values were between 0.0344 HU 1/2 and 0.0215 HU 1/2: different scanners offer varying degrees of reproducibility of noise and dose across the population. This analysis could be performed with phantoms, but phantom data would only provide information concerning specific exposure parameters for a scan: instead, a general population comparison is a way to obtain new information related to the relevant clinical adaptability of scanner models.A theoretical relationship between image noise, CTDI vol and patient size was determined based on real patient data. This relationship may provide a new index related to the scanners' adaptability concerning image quality and radiation dose across a patient population.

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Automated Technique to Measure Noise in Clinical CT Examinations

Cited by 100 publications

References 17 publications

Image noise and dose performance across a clinical population: Patient size adaptation as a metric of CT performance

Image noise and dose performance across a clinical population: Patient size adaptation as a metric of CT performance

A method to extract image noise level from patient images in CT

Adaptability index: quantifying CT tube current modulation performance from dose and quality informatics

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