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

Ria, Francesco; Wilson, Joshua M.; Zhang, Yakun; Samei, Ehsan

doi:10.1002/mp.12172

Cited by 20 publications

(9 citation statements)

References 11 publications

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“…Further, different vendors pursue different strategies to balance image quality and radiation dose across patient populations. [ The data reported in this work are in line with the theme of previously reported nonlinear relationships. Figure 2 and Figure 3 clearly show how, for Scanner 1, dose increases with patient size while noise does not show similar trend, but by comparison, both dose and noise increase with patient size in Scanner 2.…”

Section: Discussionsupporting

confidence: 92%

“…However, such relationships are properly referenced and represent reasonable models to describe the nonlinear relationships between radiation dose, image quality, and patient habitus introduced by ATCM systems in modern CT scanners. 5,11,23,24…”

Section: Discussionmentioning

confidence: 99%

“…The noise (σ) likewise is expected to take an analogous form [

σ = c \cdot e^{d \cdot E D^{2}},

where a, b, c, and d are fitting parameters.…”

Section: Methodsmentioning

confidence: 99%

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Technical Note: Validation of TG 233 phantom methodology to characterize noise and dose in patient CT data

et al. 2020

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Purpose: Phantoms are useful tools in diagnostic CT, but practical limitations reduce phantoms to being only a limited patient surrogate. Furthermore, a phantom with a single cross sectional area cannot be used to evaluate scanner performance in modern CT scanners that use dose reduction techniques such as automated tube current modulation (ATCM) and iterative reconstruction (IR) algorithms to adapt x-ray flux to patient size, reduce radiation dose, and achieve uniform image noise. A new multisized phantom (Mercury Phantom, MP) has been introduced, representing multiple diameters. This work aimed to ascertain if measurements from MP can predict radiation dose and image noise in clinical CT images to prospectively inform protocol design. Methods: The adult MP design included four different physical diameters (18.5, 23.0, 30.0, and 37.0 cm) representing a range of patient sizes. The study included 1457 examinations performed on two scanner models from two vendors, and two clinical protocols (abdominopelvic with and chest without contrast). Attenuating diameter, radiation dose, and noise magnitude (average pixel standard deviation in uniform image) was automatically estimated in patients and in the MP using a previously validated algorithm. An exponential fit of CTDI vol and noise as a function of size was applied to patients and MP data. Lastly, the fit equations from the phantom data were used to fit the patient data. In each patient distribution fit, the normalized root mean square error (nRMSE) values were calculated in the residuals' plots as a metric to indicate how well the phantom data can predict dose and noise in clinical operations as a function of size. Results: For dose across patient size distributions, the difference between nRMSE from patient fit and MP model data prediction ranged between 0.6% and 2.0% (mean 1.2%). For noise across patient size distributions, the nRMSE difference ranged between 0.1% and 4.7% (mean 1.4%). Conclusions: The Mercury Phantom provided a close prediction of radiation dose and image noise in clinical patient images. By assessing dose and image quality in a phantom with multiple sizes, protocol parameters can be designed and optimized per patient size in a highly constrained setup to predict clinical scanner and ATCM system performance.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Technical Note: Validation of TG 233 phantom methodology to characterize noise and dose in patient CT data

et al. 2020

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“…Imaging physicists can develop DL models with DLAE to perform daily clinical tasks, find the best model for a given QC application, and potentially deploy the application in a QC software program. Moreover, a move toward clinical image-based measurements as opposed to phantom-based measurements for medical imaging informatics applications is underway [ 75 – 79 ]. DLAE can serve as a tool to incorporate DL into these efforts to make use of the many benefits of modern DL techniques.…”

Section: Impact and Discussionmentioning

confidence: 99%

Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging

et al. 2019

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Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was developed to provide a code-free approach to DL for medical imaging applications. DLAE supports several DL techniques used in medical imaging, including convolutional neural networks, fully convolutional networks, generative adversarial networks, and bounding box detectors. Several example applications using clinical images were developed and tested to demonstrate the capabilities of DLAE. Additionally, a model deployment example was demonstrated in which DLAE was used to integrate two trained models into a commercial clinical software package.

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“…Anthropomorphic phantom‐based tests tend to provide better insights into image quality, but the phantom still does not fully represent the complexity of a patient. Furthermore, modern CT scanners that employ dose reduction techniques such as Automatic Tube Current Modulation (ATCM), nonlinear iterative reconstruction, and/or postprocessing techniques, produce images with variable quality depending on the object‐specific properties . Thus, the framework of monitoring the quality of CT imaging primarily through phantom‐based measurements is not optimal for assuring the quality of clinical CT images.…”

Section: Introductionmentioning

confidence: 99%

Validation of algorithmic CT image quality metrics with preferences of radiologists

et al. 2019

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Purpose: Automated assessment of perceptual image quality on clinical Computed Tomography (CT) data by computer algorithms has the potential to greatly facilitate data-driven monitoring and optimization of CT image acquisition protocols. The application of these techniques in clinical operation requires the knowledge of how the output of the computer algorithms corresponds to clinical expectations. This study addressed the need to validate algorithmic image quality measurements on clinical CT images with preferences of radiologists and determine the clinically acceptable range of algorithmic measurements for abdominal CT examinations. Materials and methods: Algorithmic measurements of image quality metrics (organ HU, noise magnitude, and clarity) were performed on a clinical CT image dataset with supplemental measures of noise power spectrum from phantom images using techniques developed previously. The algorithmic measurements were compared to clinical expectations of image quality in an observer study with seven radiologists. Sets of CT liver images were selected from the dataset where images in the same set varied in terms of one metric at a time. These sets of images were shown via a web interface to one observer at a time. First, the observer rank ordered the CT images in a set according to his/her preference for the varying metric. The observer then selected his/her preferred acceptable range of the metric within the ranked images. The agreement between algorithmic and observer rankings of image quality were investigated and the clinically acceptable image quality in terms of algorithmic measurements were determined. Results: The overall rank-order agreements between algorithmic and observer assessments were 0.90, 0.98, and 1.00 for noise magnitude, liver parenchyma HU, and clarity, respectively. The results indicate a strong agreement between the algorithmic and observer assessments of image quality. Clinically acceptable thresholds (median) of algorithmic metric values were (17.8, 32.6) HU for noise magnitude, (92.1, 131.9) for liver parenchyma HU, and (0.47, 0.52) for clarity. Conclusions: The observer study results indicated that these algorithms can robustly assess the perceptual quality of clinical CT images in an automated fashion. Clinically acceptable ranges of algorithmic measurements were determined. The correspondence of these image quality assessment algorithms to clinical expectations paves the way toward establishing diagnostic reference levels in terms of clinically acceptable perceptual image quality and data-driven optimization of CT image acquisition protocols.

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

Cited by 20 publications

References 11 publications

Technical Note: Validation of TG 233 phantom methodology to characterize noise and dose in patient CT data

Technical Note: Validation of TG 233 phantom methodology to characterize noise and dose in patient CT data

Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging

Validation of algorithmic CT image quality metrics with preferences of radiologists

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