A proposed protocol for acceptance and constancy control of computed tomography systems: A Nordic Association for Clinical Physics (NACP) work group report

Self Cite

The purpose of this study was to develop a method of performing routine periodical quality controls (QC) of CT systems by automatically analyzing key performance indicators (KPIs), obtainable from images of manufacturers' quality assurance (QA) phantoms. A KPI pertains to a measurable or determinable QC parameter that is influenced by other underlying fundamental QC parameters. The established KPIs are based on relationships between existing QC parameters used in the annual testing program of CT scanners at the Karolinska University Hospital in Stockholm, Sweden. The KPIs include positioning, image noise, uniformity, homogeneity, the CT number of water, and the CT number of air. An application (MonitorCT) was developed to automatically evaluate phantom images in terms of the established KPIs. The developed methodology has been used for two years in clinical routine, where CT technologists perform daily scans of the manufacturer's QA phantom and automatically send the images to MonitorCT for KPI evaluation. In the cases where results were out of tolerance, actions could be initiated in less than 10 min. 900 QC scans from two CT scanners have been collected and analyzed over the two‐year period that MonitorCT has been active. Two types of errors have been registered in this period: a ring artifact was discovered with the image noise test, and a calibration error was detected multiple times with the CT number test. In both cases, results were outside the tolerances defined for MonitorCT, as well as by the vendor. Automated monitoring of KPIs is a powerful tool that can be used to supplement established QC methodologies. Medical physicists and other professionals concerned with the performance of a CT system will, using such methods, have access to comprehensive data on the current and historical (trend) status of the system such that swift actions can be taken in order to ensure the quality of the CT examinations, patient safety, and minimal disruption of servicePACS numbers: 87.57.C‐, 87.57.N‐, 87.57.Q‐

“…This was in accordance with the tolerances defined by the manufacturer and is also what the Nordic Association of Clinical Physics (NACP) and ACR recommend 1 , 11 …”

Section: Methodssupporting

confidence: 88%

“…The CT number tolerance used during annual quality controls at the hospital is

\pm 4 HU

for water and

\pm 10 HU

Section: Methodsmentioning

confidence: 60%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Quality control of CT systems by automated monitoring of key performance indicators: a two‐year study

Nowik

Bujila

Poludniowski

et al. 2015

Self Cite

“…The Nordic guidance on CT QA (13) lists CT AEC testing as an optional test and it is suggested that a cone‐shaped or anthropomorphic phantom could be used to assess the functionality of the AEC system, via noise measurements along the length of the phantom. CT AEC testing is one of the five major test areas in the Belgian CT QA protocol, (14) and the authors describe a methodology to allow the independent assessment of the longitudinal and rotational aspects of the AEC system.…”

Section: Introductionmentioning

confidence: 99%

A routine quality assurance test for CT automatic exposure control systems

Iball

Moore

Crawford

2016

The study purpose was to develop and validate a quality assurance test for CT automatic exposure control (AEC) systems based on a set of nested polymethylmethacrylate CTDI phantoms. The test phantom was created by offsetting the 16 cm head phantom within the 32 cm body annulus, thus creating a three part phantom. This was scanned at all acceptance, routine, and some nonroutine quality assurance visits over a period of 45 months, resulting in 115 separate AEC tests on scanners from four manufacturers. For each scan the longitudinal mA modulation pattern was generated and measurements of image noise were made in two annular regions of interest. The scanner displayed CTDIvol and DLP were also recorded. The impact of a range of AEC configurations on dose and image quality were assessed at acceptance testing. For systems that were tested more than once, the percentage of CTDIvol values exceeding 5%, 10%, and 15% deviation from baseline was 23.4%, 12.6%, and 8.1% respectively. Similarly, for the image noise data, deviations greater than 2%, 5%, and 10% from baseline were 26.5%, 5.9%, and 2%, respectively. The majority of CTDIvol and noise deviations greater than 15% and 5%, respectively, could be explained by incorrect phantom setup or protocol selection. Barring these results, CTDIvol deviations of greater than 15% from baseline were found in 0.9% of tests and noise deviations greater than 5% from baseline were found in 1% of tests. The phantom was shown to be sensitive to changes in AEC setup, including the use of 3D, longitudinal or rotational tube current modulation. This test methodology allows for continuing performance assessment of CT AEC systems, and we recommend that this test should become part of routine CT quality assurance programs. Tolerances of ±15% for CTDIvol and ±5% for image noise relative to baseline values should be used.PACS number(s): 87.57.Q‐

100 days with scans of the same Catphan phantom on the same CT scanner

Husby

Svendsen

Andersen

et al. 2017

Quality control (QC) of CT scanners is important to evaluate image quality and radiation dose. Different QC phantoms for testing image quality parameters on CT are commercially available, and Catphan phantoms are widely used for this purpose. More data from measured image quality parameters on CT are necessary to assess test methods, tolerance levels, and test frequencies. The aim of this study was to evaluate the stability of essential image quality parameters for axial and helical scans on one CT scanner over time. A Catphan 600 phantom was scanned on a Philips Ingenuity CT scanner for 100 days over a period of 6 months. At each day of testing, one helical scan covering the entire phantom and four axial scans covering four different modules in the phantom were performed. All images were uploaded into Image Owl for automatic analysis of CT numbers, modular transfer function (MTF), low‐contrast resolution, noise, and uniformity. In general, the different image quality parameters for both scan techniques were stable over time compared to given tolerance levels. Average measured CT numbers differed between axial and helical scans, while MTF was almost identical for helical and axial scans. Axial scans had better low‐contrast resolution and less noise than helical scans. The uniformity was relatively similar for axial and helical scans. Most standard deviations of measured values were larger for helical scans compared to axial scans. Test results in this study were stable over time for both scan techniques, but further studies on different CT scanners are required to confirm that this also holds true for other systems.