Performance Evaluation of Computed Tomography Systems - The Report of AAPM Task Group 233

Samei, Ehsan; Bakalyar, Donovan; Boedeker, Kirsten; Brady, Samuel L.; Fan, Jiahua; Leng, Shuai; Myers, Kyle J.; Popescu, Lucreţiu M.; Ramírez-Giraldo, Juan Carlos; Ranallo, Frank N.; Solomon, Justin; Vaishnav, J. Y.; Wang, Jia

doi:10.37206/186

Cited by 32 publications

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“…Observers were allowed to adjust the window and level values and image magnification factor for each study but were not allowed to modify the luminosity of the monitor, which was calibrated to a maximum luminance of 400 cd/m 2 in accordance with the American Association of Physicists in Medicine (AAPM) Task Group 18 report guidelines for diagnostic monitor calibration and QC. 24 Ambient room lighting remained unchanged for each observer. Each observer was asked to record the number of spokes detected in the lowest contrast axial image and to record this value.…”

Section: B2 Algorithm Performance Versus Human Observersmentioning

confidence: 99%

Automated low‐contrast pattern recognition algorithm for magnetic resonance image quality assessment

et al. 2017

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Section: B2 Algorithm Performance Versus Human Observersmentioning

confidence: 99%

Automated low‐contrast pattern recognition algorithm for magnetic resonance image quality assessment

et al. 2017

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“…Variation of CT numbers within acquisition slab central image of the uniform CTP486 module was assessed through the non-uniformity index (NUI). In particular, NUI was estimated by adapting the method proposed by Li et al and suggested by the AAPM TG233 report for assessing spatial non-uniformity of noise maps [ 38 , 77 ]. Images of the entire phantom were divided into M = 249 small ROIs of 7 mm × 7 mm size.…”

Section: Methodsmentioning

confidence: 99%

A comprehensive assessment of physical image quality of five different scanners for head CT imaging as clinically used at a single hospital centre—A phantom study

et al. 2021

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Nowadays, given the technological advance in CT imaging and increasing heterogeneity in characteristics of CT scanners, a number of CT scanners with different manufacturers/technologies are often installed in a hospital centre and used by various departments. In this phantom study, a comprehensive assessment of image quality of 5 scanners (from 3 manufacturers and with different models) for head CT imaging, as clinically used at a single hospital centre, was hence carried out. Helical and/or sequential acquisitions of the Catphan-504 phantom were performed, using the scanning protocols (CTDIvol range: 54.7–57.5 mGy) employed by the staff of various Radiology/Neuroradiology departments of our institution for routine head examinations. CT image quality for each scanner/acquisition protocol was assessed through noise level, noise power spectrum (NPS), contrast-to-noise ratio (CNR), modulation transfer function (MTF), low contrast detectability (LCD) and non-uniformity index analyses. Noise values ranged from 3.5 HU to 5.7 HU across scanners/acquisition protocols. NPS curves differed in terms of peak position (range: 0.21–0.30 mm-1). A substantial variation of CNR values with scanner/acquisition protocol was observed for different contrast inserts. The coefficient of variation (standard deviation divided by mean value) of CNR values across scanners/acquisition protocols was 18.3%, 31.4%, 34.2%, 30.4% and 30% for teflon, delrin, LDPE, polystyrene and acrylic insert, respectively. An appreciable difference in MTF curves across scanners/acquisition protocols was revealed, with a coefficient of variation of f50%/f10% of MTF curves across scanners/acquisition protocols of 10.1%/7.4%. A relevant difference in LCD performance of different scanners/acquisition protocols was found. The range of contrast threshold for a typical object size of 3 mm was 3.7–5.8 HU. Moreover, appreciable differences in terms of NUI values (range: 4.1%-8.3%) were found. The analysis of several quality indices showed a non-negligible variability in head CT imaging capabilities across different scanners/acquisition protocols. This highlights the importance of a physical in-depth characterization of image quality for each CT scanner as clinically used, in order to optimize CT imaging procedures.

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“…We used a prototype Mercury Phantom (currently available from GAMMEX Sun Nuclear Corporation, Middleton WI),[ updated and adapted by the AAPM Task Group 233. [ The phantom design, shown in Fig. 1, included five cylinders made of virgin ultra‐high molecular weight polyethylene (density 0.93 g/cm 3 ) with physical diameters of 12.0, 18.5, 23.0, 30.0, and 37.0 cm representing different patient body regions and size ranges.…”

Section: Methodsmentioning

confidence: 99%

“…American Association of Physicists in Medicine Task Group 233 recently published a list of commercially available phantoms that may be useful for CT performance evaluation, including multisized and anthropomorphic phantoms. [ This scenario is further complicated by CT scanner dose reduction techniques, such as automated tube current modulation (ATCM) and iterative reconstruction (IR) algorithms that have changed the classic, statistical relationships between radiation dose and image quality. [ In particular, modern CT scanners implement ATCM systems to adjust x‐ray flux for patient size to save patient dose and achieve some measure of uniform image quality.…”

Section: Introductionmentioning

confidence: 99%

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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Performance Evaluation of Computed Tomography Systems - The Report of AAPM Task Group 233

Cited by 32 publications

References 0 publications

Automated low‐contrast pattern recognition algorithm for magnetic resonance image quality assessment

Automated low‐contrast pattern recognition algorithm for magnetic resonance image quality assessment

A comprehensive assessment of physical image quality of five different scanners for head CT imaging as clinically used at a single hospital centre—A phantom study

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

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