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A novel routine dual‐energy computed tomography (DECT) quality control (QC) program was developed to address the current deficiency of routine QC for this technology. The dual‐energy quality control (DEQC) program features (1) a practical phantom with clinically relevant materials and concentrations, (2) a clinically relevant acquisition, reconstruction, and postprocessing protocol, and (3) a fully automated analysis software to extract quantitative data for database storage and trend analysis. The phantom, designed for easy set up for standalone or adjacent imaging next to the ACR phantom, was made in collaboration with an industry partner and informed by clinical needs to have four iodine inserts (0.5, 1, 2, and 5 mg/ml) and one calcium insert (100 mg/ml) equally spaced in a cylindrical water‐equivalent background. The imaging protocol was based on a clinical DECT abdominal protocol capable of producing material specific concentration maps, virtual unenhanced images, and virtual monochromatic images. The QC automated analysis software uses open‐source technologies which integrates well with our current automated CT QC database. The QC program was tested on a GE 750 HD scanner and two Siemens SOMATOM FLASH scanners over a 3‐month period. The automated algorithm correctly identified the appropriate region of interest (ROI) locations and stores measured values in a database for monitoring and trend analysis. Slight variations in protocol settings were noted based on manufacturer. Overall, the project proved to provide a convenient and dependable clinical tool for routine oversight of DE CT imaging within the clinic. [ABSTRACT FROM AUTHOR]

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 CTDIvol 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. [ABSTRACT FROM AUTHOR]

Purpose: The task‐based assessment of image quality using model observers is increasingly used for the assessment of different imaging modalities. However, the performance computation of model observers needs standardization as well as a well‐established trust in its implementation methodology and uncertainty estimation. The purpose of this work was to determine the degree of equivalence of the channelized Hotelling observer performance and uncertainty estimation using an intercomparison exercise. Materials and Methods: Image samples to estimate model observer performance for detection tasks were generated from two‐dimensional CT image slices of a uniform water phantom. A common set of images was sent to participating laboratories to perform and document the following tasks: (a) estimate the detectability index of a well‐defined CHO and its uncertainty in three conditions involving different sized targets all at the same dose, and (b) apply this CHO to an image set where ground truth was unknown to participants (lower image dose). In addition, and on an optional basis, we asked the participating laboratories to (c) estimate the performance of real human observers from a psychophysical experiment of their choice. Each of the 13 participating laboratories was confidentially assigned a participant number and image sets could be downloaded through a secure server. Results were distributed with each participant recognizable by its number and then each laboratory was able to modify their results with justification as model observer calculation are not yet a routine and potentially error prone. Results: Detectability index increased with signal size for all participants and was very consistent for 6 mm sized target while showing higher variability for 8 and 10 mm sized target. There was one order of magnitude between the lowest and the largest uncertainty estimation. Conclusions: This intercomparison helped define the state of the art of model observer performance computation and with thirteen participants, reflects openness and trust within the medical imaging community. The performance of a CHO with explicitly defined channels and a relatively large number of test images was consistently estimated by all participants. In contrast, the paper demonstrates that there is no agreement on estimating the variance of detectability in the training and testing setting. [ABSTRACT FROM AUTHOR]

Purpose: To quantitatively compare noise texture across computed tomography (CT) scanners from different manufacturers using the noise power spectrum (NPS). Methods: The American College of Radiology CT accreditation phantom (Gammex 464, Gammex, Inc., Middleton, WI) was imaged on two scanners: Discovery CT 750HD (GE Healthcare, Waukesha, WI), and SOMATOM Definition Flash (Siemens Healthcare, Germany), using a consistent acquisition protocol (120 kVp, 0.625/0.6 mm slice thickness, 250 mAs, and 22 cm field of view). Images were reconstructed using filtered backprojection and a wide selection of reconstruction kernels. For each image set, the 2D NPS were estimated from the uniform section of the phantom. The 2D spectra were normalized by their integral value, radially averaged, and filtered by the human visual response function. A systematic kernel-by-kernel comparison across manufacturers was performed by computing the root mean square difference (RMSD) and the peak frequency difference (PFD) between the NPS from different kernels. GE and Siemens kernels were compared and kernel pairs that minimized the RMSD and |PFD| were identified. Results: The RMSD (|PFD|) values between the NPS of GE and Siemens kernels varied from 0.01 mm2 (0.002 mm-1) to 0.29 mm2 (0.74 mm-1). The GE kernels 'Soft,' 'Standard,' 'Chest,' and 'Lung' closely matched the Siemens kernels 'B35f,' 'B43f,' 'B41f,' and 'B80f' (RMSD < 0.05 mm2, |PFD| < 0.02 mm-1, respectively). The GE 'Bone,' 'Bone+,' and 'Edge' kernels all matched most closely with Siemens 'B75f' kernel but with sizeable RMSD and |PFD| values up to 0.18 mm2 and 0.41 mm-1, respectively. These sizeable RMSD and |PFD| values corresponded to visually perceivable differences in the noise texture of the images. Conclusions: It is possible to use the NPS to quantitatively compare noise texture across CT systems. The degree to which similar texture across scanners could be achieved varies and is limited by the kernels available on each scanner. [ABSTRACT FROM AUTHOR]

Photon-counting CT detectors are the next step in advancing CT system development and will replace the current energy integrating detectors (EID) in CT systems in the near future. In this context, the performance of PCCT was compared to EID CT for three clinically relevant tasks: abdominal soft tissue imaging, where differentiating low contrast features is important; vascular imaging, where iodine detectability is critical; and, high-resolution skeletal and lung imaging. A multi-tiered phantom was imaged on an investigational clinical PCCT system (Siemens Healthineers) across different doses using three imaging modes: macro and ultra-high resolution (UHR) PCCT modes and EID CT. Images were reconstructed using filtered backprojection and soft tissue (B30f), vascular (B46f), or high-resolution (B70f; U70f for UHR) kernels. Noise power spectra, task transfer functions, and detectability index were evaluated. For a soft tissue task, PCCT modes showed comparable noise and resolution with improved contrast-to-noise ratio. For a vascular task, PCCT modes showed lower noise and improved iodine detectability. For a high resolution task, macro mode showed lower noise and comparable resolution while UHR mode showed higher noise but improved spatial resolution for both air and bone. PCCT offers competitive advantages to EID CT for clinical tasks.

OBJECTIVE. Diagnostic reference levels were developed as guidance for radiation dose in medical imaging and, by inference, diagnostic quality. The objective of this work was to expand the concept of diagnostic reference levels to explicitly include noise of CT examinations to simultaneously target both dose and quality through corresponding reference values. MATERIALS AND METHODS. The study consisted of 2851 adult CT examinations performed with scanners from two manufacturers and two clinical protocols: abdominopelvic CT with IV contrast administration and chest CT without IV contrast administration. An institutional informatics system was used to automatically extract protocol type, patient diameter, volume CT dose index, and noise magnitude from images. The data were divided into five reference patient size ranges. Noise reference level, noise reference range, dose reference level, and dose reference range were defined for each size range. RESULTS. The data exhibited strong dependence between dose and patient size, weak dependence between noise and patient size, and different trends for different manufacturers with differing strategies for tube current modulation. The results suggest size-based reference intervals and levels for noise and dose (e.g., noise reference level and noise reference range of 11.5-12.9 HU and 11.0-14.0 HU for chest CT and 10.1-12.1 HU and 9.4-13.7 HU for abdominopelvic CT examinations) that can be targeted to improve clinical performance consistency. CONCLUSION. New reference levels and ranges, which simultaneously consider image noise and radiation dose information across wide patient populations, were defined and determined for two clinical protocols. The methods of new quantitative constraints may provide unique and useful information about the goal of managing the variability of image quality and dose in clinical CT examinations.

Purpose: To create and validate a systematic observer performance platform for evaluation of simulated liver lesions at pediatric CT and to test this paradigm to measure the effect of radiation dose reduction on detection performance and reader confidence., Materials and Methods: Thirty normal pediatric (from patients aged 0-10 years) contrast material-enhanced, de-identified abdominal CT scans obtained from July 1, 2012, through July 1, 2016, were retrospectively collected from the clinical database. The study was exempt from institutional review board approval. Zero to three simulated, low-contrast liver lesions (≤6 mm) were digitally inserted by using software, and noise was added to simulate reductions in volume CT dose index (representing radiation dose estimation) of 25% and 50%. Pediatric, abdominal, and resident radiologists (three of each) reviewed 90 data sets in three sessions using an online interface, marking each lesion location and rating confidence (scale, 0-100). Statistical analysis was performed by using software., Results: Mixed-effects models revealed a significant decrease in detection sensitivity as radiation dose decreased ( P < .001). The mean confidence of the full-dose and 25% dose reduction examinations was significantly higher than that of the 50% dose reduction examinations ( P = .011 and .012, respectively) but not different from one another ( P = .866). Dose was not a significant predictor of time to complete each case, and subspecialty was not a significant predictor of sensitivity or false-positive results., Conclusion: Sensitivity for lesion detection significantly decreased as dose decreased; however, confidence did not change between the full-dose and 25% reduced-dose scans. This suggests that readers are unaware of this decrease in performance, which should be accounted for in clinical dose reduction efforts. Keywords: Abdomen/GI, CT, Liver, Observer Performance, Pediatrics, Perception Image© RSNA, 2019., Competing Interests: Disclosures of Conflicts of Interest: J.S.N. disclosed no relevant relationships. J.B.S. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: receives royalties (<$5000/year) from a license deal between Duke University and Sun Nuclear Gammex for the design of a CT image quality phantom. Other relationships: disclosed no relevant relationships. E.S. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: paid by Imalogix for board membership and consultancy; paid by for expert testimony (patent law); institution receives grants from Siemens and Bracco; institution receives royalties from GE, Imalogix, 12Sigma, SunNuclear, Cambridge University Press, and Wiley and Sons. Other relationships: disclosed no relevant relationships. T.R. disclosed no relevant relationships. L.N. disclosed no relevant relationships. A.E. disclosed no relevant relationships. B.Z. disclosed no relevant relationships. B.C.A. disclosed no relevant relationships. J.T.D. disclosed no relevant relationships. A.D. disclosed no relevant relationships. R.G. disclosed no relevant relationships. D.M. disclosed no relevant relationships. C.M.M. disclosed no relevant relationships. V.P. disclosed no relevant relationships. B.N.P. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution receives grant from Siemens; author receives grant from GE Healthcare; author receives payment for lectures from GE Healthcare. Other relationships: disclosed no relevant relationships. G.R.S. disclosed no relevant relationships. D.P.F. disclosed no relevant relationships., (2019 by the Radiological Society of North America, Inc.)

Objective: Modern CT systems use surrogates of noise-noise index (NI) and quality reference effective tube current-time product (Q)-to infer the quality of images acquired using tube current modulation. This study aimed to determine the relationship between actual noise and these surrogates for two CT scanners from two different manufacturers., Materials and Methods: Two phantoms (adult and 1-year-old child) were imaged on two CT scanners (64 and 128 MDCT) using a clinical range of NI (6-22) and Q (30-300 mA). Each scan was performed twice, and noise was measured in the mediastinum, lung, and abdomen using an image subtraction technique. The effect on noise from changing other imaging parameters, such as beam collimation, pitch, peak kilovoltage, slice thickness, FOV, reconstruction kernel or algorithm, and patient age category (adult or pediatric), was investigated., Results: On the 64-MDCT scanner, noise increased linearly along with NI, with the slope affected by changing the anatomy of interest, peak kilovoltage, reconstruction algorithm, and convolution kernel. The noise-NI relationship was independent of phantom size, slice thickness, pitch, FOV, and beam width. On the 128-MDCT scanner, noise decreased nonlinearly along with increasing Q, slice thickness, and peak tube voltage. The noise-Q relationship also depended on anatomy of interest, phantom size, age selection, and reconstruction algorithm but was independent of pitch, FOV, and detector configuration., Conclusion: We established how noise changes with changing image quality indicators across a clinically relevant range of imaging parameters. This work can aid in optimizing protocols by targeting specific noise levels for different types of CT examinations.