Applying a New CT Quality Metric in Radiology: How CT Pulmonary Angiography Repeat Rates Compare Across Institutions

Rose, Sean; Viggiano, Ben; Bour, Robert K.; Bartels, Carrie; Kanne, J.P.; Szczykutowicz, Timothy P.

doi:10.1016/j.jacr.2021.02.014

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…The algorithm can be further developed into a continuous IQ monitoring tool that may prove invaluable in radiation dose and contrast injection protocol optimization 29,30 or in retake scan decision making in daily clinical practice. 31…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, these results are encouraging and future studies can be undertaken to implement the algorithm for different anatomies and slice reconstructions, as well as to evaluate IQ metric performance when using CT scanner settings that might be expected to interfere. The algorithm can be further developed into a continuous IQ monitoring tool that may prove invaluable in radiation dose and contrast injection protocol optimization 29,30 or in retake scan decision making in daily clinical practice 31 …”

Section: Discussionmentioning

confidence: 99%

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A New Algorithm for Automatically Calculating Noise, Spatial Resolution, and Contrast Image Quality Metrics

et al. 2023

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ObjectivesThe aims of this study were to develop a proof-of-concept computer algorithm to automatically determine noise, spatial resolution, and contrast-related image quality (IQ) metrics in abdominal portal venous phase computed tomography (CT) imaging and to assess agreement between resulting objective IQ metrics and subjective radiologist IQ ratings.Materials and MethodsAn algorithm was developed to calculate noise, spatial resolution, and contrast IQ parameters. The algorithm was subsequently used on 2 datasets of anthropomorphic phantom CT scans, acquired on 2 different scanners (n = 57 each), and on 1 dataset of patient abdominal CT scans (n = 510). These datasets include a range of high to low IQ: in the phantom dataset, this was achieved through varying scanner settings (tube voltage, tube current, reconstruction algorithm); in the patient dataset, lower IQ images were obtained by reconstructing 30 consecutive portal venous phase scans as if they had been acquired at lower mAs. Five noise, 1 spatial, and 13 contrast parameters were computed for the phantom datasets; for the patient dataset, 5 noise, 1 spatial, and 18 contrast parameters were computed. Subjective IQ rating was done using a 5-point Likert scale: 2 radiologists rated a single phantom dataset each, and another 2 radiologists rated the patient dataset in consensus. General agreement between IQ metrics and subjective IQ scores was assessed using Pearson correlation analysis. Likert scores were grouped into 2 categories, “insufficient” (scores 1–2) and “sufficient” (scores 3–5), and differences in computed IQ metrics between these categories were assessed using the Mann-Whitney U test.ResultsThe algorithm was able to automatically calculate all IQ metrics for 100% of the included scans. Significant correlations with subjective radiologist ratings were found for 4 of 5 noise (R2 range = 0.55–0.70), 1 of 1 spatial resolution (R2 = 0.21 and 0.26), and 10 of 13 contrast (R2 range = 0.11–0.73) parameters in the phantom datasets and for 4 of 5 noise (R2 range = 0.019–0.096), 1 of 1 spatial resolution (R2 = 0.11), and 16 of 18 contrast (R2 range = 0.008–0.116) parameters in the patient dataset. Computed metrics that significantly differed between “insufficient” and “sufficient” categories were 4 of 5 noise, 1 of 1 spatial resolution, 9 and 10 of 13 contrast parameters for phantom the datasets and 3 of 5 noise, 1 of 1 spatial resolution, and 10 of 18 contrast parameters for the patient dataset.ConclusionThe developed algorithm was able to successfully calculate objective noise, spatial resolution, and contrast IQ metrics of both phantom and clinical abdominal CT scans. Furthermore, multiple calculated IQ metrics of all 3 categories were in agreement with subjective radiologist IQ ratings and significantly differed between “insufficient” and “sufficient” IQ scans. These results demonstrate the feasibility and potential of algorithm-determined objective IQ. Such an algorithm should be applicable to any scan and may help in optimization and quality control...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A New Algorithm for Automatically Calculating Noise, Spatial Resolution, and Contrast Image Quality Metrics

et al. 2023

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“…The difference in Q-ICE value, however, was 4.8 HU, which is roughly half of the SD in the Q-ICE values for the sites. Although the technologists at the 2 sites are part of the same institution and receive similar training, they have been previously shown to differ slightly in their timelines for acute stroke 13 and rates of repeated CT examinations, 14 which implies there could be a difference in contrast administration practices. Scanner-specific CT number issues could also have accounted for the difference we observed 15,16 .…”

Section: Discussionmentioning

confidence: 99%

A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography

Szczykutowicz

Viggiano

Rose³

et al. 2021

J Comput Assist Tomogr

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Background: Poor contrast enhancement is related to issues with examination execution, contrast prescription, computed tomography (CT) protocols, and patient conditions. Currently, our community has no metric to monitor true enhancement on routine single-phase examinations because this requires knowledge of both pre-and postcontrast CT number. Purpose:We propose an automatable solution to quantifying contrast enhancement without requiring a dedicated noncontrast series. Methods:The difference in CT number between a target region in an enhanced and unenhanced image defines the metric "quantification of iodine contrast enhancement" (Q-ICE). Quantification of iodine contrast enhancement uses the noncontrast bolus tracking baseline image from routine abdominal examinations, which mitigates the need for a dedicated noncontrast series. We applied this method retrospectively to 312 patient livers from 2 sites between 2017 and 2020. Each site used a weight-based contrast injection protocol for weights 60 to 113 kg and a constant volume less than 60 kg and greater than 113 kg. Hypothesis testing was performed to compare Q-ICE between sites and detect Q-ICE dependence on weight and kilovoltage (kV).Results: Mean Q-ICE differed between sites (P = 0.004) by 4.96 Hounsfield unit with 95% confidence interval (1.63-8.28), albeit this difference was roughly 2 times smaller than the SD in Q-ICE across patients at a single site. For patients between 60 and 113 kg, we did not observe evidence of Q-ICE varying with patient weight (P = 0.920 and 0.064 for 120 and 140 kV, respectively). The Q-ICE did vary with patient weight for patients less than 60 kg (P = 0.003) and greater than 113 kg (P = 0.04). We observed a roughly 10 Hounsfield unit reduction in Q-ICE liver for patients scanned with 140 versus 120 kV. We observed several underenhancing examinations with an arterial phase appearance motivating our CT protocol optimization team to consider increasing the delay for slowly enhancing patients.Conclusions: A quality metric for quantifying CT contrast enhancement was developed and suggested tangible opportunities for quality improvement and potential financial savings.

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“…A rejected image refers to an acquisition that is considered unsatisfactory in terms of its quality by the radiographer at the time of capture. The radiographer takes another image fulfilling the technical requirements necessary for an accurate diagnosis [7,8]. Some studies refers to these images as repeats [8].…”

Section: Introductionmentioning

confidence: 99%

“…The radiographer takes another image fulfilling the technical requirements necessary for an accurate diagnosis [7,8]. Some studies refers to these images as repeats [8]. Reject analysis in digital radiography (DR) is an important part of quality assurance.…”

Section: Introductionmentioning

confidence: 99%

Reject Analysis in Digital Radiography and Computed Tomography: A Belgian Imaging Department Case Study

Haddad,

Saleme,

Howarth

et al. 2023

JBSR

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Objective: Reject analysis is usually performed in digital radiography (DR) for quality assurance. Data for computed tomography (CT) rejects remains sparse. The aim of this study is to help provide a straightforward benchmark for reject analysis of both DR and CT. Materials and methods: This retrospective observational study included 107,277 DR and 20,659 CT during 18 months in a tertiary care center. Rejected acquisitions were retrieved by Dose Archiving and Communication System (DACS). The DR and CT reject analysis included reject rates, reasons for rejection and supplementary radiation dose associated with these rejects. Results: 8,904 rejected DR and 514 rejected CT were retrieved. The DR reject rate was 8.3% whereas the CT reject rate was 2.5%. The cumulative effective dose (ED) of DR rejects was 377.3 mSv while the cumulative ED of CT rejects was 1267.4 mSv. The major reason for rejects was positioning for both DR (61%) and CT (44%). Conclusion: This study helps constitute a simple reproducible method to analyze both DR and CT rejects simultaneously. Although CT rejects are less often monitored than DR rejects, the radiation dose associated with CT rejects is much higher, which emphasizes the need to systematically monitor both DR and CT rejects. Investigating the reasons and the most frequently rejected examinations gives an opportunity for improvement of imaging techniques in cooperation with technologists.

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Applying a New CT Quality Metric in Radiology: How CT Pulmonary Angiography Repeat Rates Compare Across Institutions

Cited by 5 publications

References 33 publications

A New Algorithm for Automatically Calculating Noise, Spatial Resolution, and Contrast Image Quality Metrics

A New Algorithm for Automatically Calculating Noise, Spatial Resolution, and Contrast Image Quality Metrics

A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography

Reject Analysis in Digital Radiography and Computed Tomography: A Belgian Imaging Department Case Study

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