CT dose survey in adults: what sample size for what precision?

Taylor, Stephen; Muylem, Alain Van; Howarth, Nigel; Gevenois, Pierre Alain; Tack, Denis

doi:10.1007/s00330-016-4333-3

Cited by 27 publications

(21 citation statements)

References 13 publications

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“…Taylor et al have recently shown that samples of approximately 300 CT examinations of patients with a body weight in the range of 67–73 kg are necessary to create reliable DRLs [ 29 ]. For many years, broad application of the DRL concept has allowed national homogenisation of patient exposure in practice for specific anatomical regions [ 30 , 31 ].…”

Section: Discussionmentioning

confidence: 99%

Task-based quantification of image quality using a model observer in abdominal CT: a multicentre study

Racine

Ryckx

et al. 2018

Eur Radiol

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ObjectiveWe investigated the variability in diagnostic information inherent in computed tomography (CT) images acquired at 68 different CT units, with the selected acquisition protocols aiming to answer the same clinical question.MethodsAn anthropomorphic abdominal phantom with two optional rings was scanned on 68 CT systems from 62 centres using the local clinical acquisition parameters of the portal venous phase for the detection of focal liver lesions. Low-contrast detectability (LCD) was assessed objectively with channelised Hotelling observer (CHO) using the receiver operating characteristic (ROC) paradigm. For each lesion size, the area under the ROC curve (AUC) was calculated and considered as a figure of merit. The volume computed tomography dose index (CTDIvol) was used to indicate radiation dose exposure.ResultsThe median CTDIvol used was 5.8 mGy, 10.5 mGy and 16.3 mGy for the small, medium and large phantoms, respectively. The median AUC obtained from clinical CT protocols was 0.96, 0.90 and 0.83 for the small, medium and large phantoms, respectively.ConclusionsOur study used a model observer to highlight the difference in image quality levels when dealing with the same clinical question. This difference was important and increased with growing phantom size, which generated large variations in patient exposure. In the end, a standardisation initiative may be launched to ensure comparable diagnostic information for well-defined clinical questions. The image quality requirements, related to the clinical question to be answered, should be the starting point of patient dose optimisation.Key Points• Model observers enable to assess image quality objectively based on clinical tasks.• Objective image quality assessment should always include several patient sizes.• Clinical diagnostic image quality should be the starting point for patient dose optimisation.• Dose optimisation by applying DRLs only is insufficient for ensuring clinical requirements.

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Section: Discussionmentioning

confidence: 99%

Task-based quantification of image quality using a model observer in abdominal CT: a multicentre study

Racine

Ryckx

et al. 2018

Eur Radiol

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“…This small group size might not reflect the real dose exposure for the corresponding CT examination. Recently, Taylor et al reported a high variability for DLP in CT dose surveys depending on the protocol and patient weight [19]. Even when including 50 patients per protocol, a 95 % confidence interval lower than 10 % of the median (CI95 %/med< 10 %) was not reached for most protocols.…”

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confidence: 99%

“…Even when including 50 patients per protocol, a 95 % confidence interval lower than 10 % of the median (CI95 %/med< 10 %) was not reached for most protocols. Furthermore, for abdominal CT, n = 420 and for cervical spine n = 900 examinations were needed to reach the CI95 %/med< 10 % [19]. Only a few studies reported implementation of local DRLs based on systematic dose monitoring [1].…”

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confidence: 99%

Cloud-Based CT Dose Monitoring using the DICOM-Structured Report: Fully Automated Analysis in Regard to National Diagnostic Reference Levels

Boos

Meineke²,

Rubbert

et al. 2015

Fortschr Röntgenstr

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Ziel: Ziel dieser Studie war die Implementierung eines Cloud-basierten CT-Dosismonitorings basierend auf dem DICOM-Structured Report (DICOM-SR) zur automatischen Überwachung der Dosisexposition im Hinblick auf die nationalen diagnostischen Referenzwerte (DRW). Material und Methoden: Zur automatischen Erfassung und Überwachung der CT-Dosisdaten wurde eine neuartige, in Kooperation mitentwickelte Software basierend auf dem DICOM-SR eingesetzt. Der DICOM-SR aller CT-Untersuchungen unserer Einrichtung zwischen 09/2011 und 03/2015 wurde automatisch anonymisiert und an einen Cloud-Server verschickt. Die Daten wurden automatisch im Hinblick auf die Körper-region, das Patientenalter und den korrespondierenden DRW für den volumetrischen Computertomografie-Dosis-Index (CTDI vol ) sowie für das Dosis-Längen-Produkt (DLP) analysiert. Ergebnisse: Datensätze von 36 523 CT-Untersuchungen (131 527 Scanserien) von drei verschiedenen CT-Geräten und einem PET-CT wurden analysiert. Insgesamt betrug der mittlere CTDI vol 51,3 % und das mittlere DLP 52,8 % der nationalen DRW. Bezogen auf die nationalen DRW betrugen CTDI vol und DLP für die Abdomen-CT 43,8 % und 43,1 % (n = 10 590), für die Schädel-CT 66,6 % und 69,6 % (n = 16 098) und für die Thorax-CT 37,8 % and 44,0 % (n = 10 387). Insgesamt überschritten 1,9 % der CT-Untersuchungen den CTDI vol und 2,9 % der Untersuchungen das DLP der nationalen DRW. Zwischen unterschiedlichen CT-Protokollen, die dem gleichen nationalen DRW zugeordnet wurden, variierte die Strahlenexposition um bis zu 50 %. Schlussfolgerung: Das implementierte, Cloudbasierte CT-Dosismonitoring basierend auf dem DICOM-SR ermöglicht eine automatische, umfassende Benchmarkanalyse im Hinblick auf die nationalen DRW. Insgesamt betrug die DosisexposiAbstract ! Purpose: To implement automated CT dose data monitoring using the DICOM-Structured Report (DICOM-SR) in order to monitor dose-related CT data in regard to national diagnostic reference levels (DRLs). Materials and Methods: We used a novel in-house co-developed software tool based on the DICOM-SR to automatically monitor dose-related data from CT examinations. The DICOM-SR for each CT examination performed between 09/2011 and 03/2015 was automatically anonymized and sent from the CT scanners to a cloud server. Data was automatically analyzed in accordance with body region, patient age and corresponding DRL for volumetric computed tomography dose index (CTDI vol ) and dose length product (DLP). Results: Data of 36 523 examinations (131 527 scan series) performed on three different CT scanners and one PET/CT were analyzed. The overall mean CTDI vol and DLP were 51.3 % and 52.8 % of the national DRLs, respectively. CTDI vol and DLP reached 43.8 % and 43.1 % for abdominal CT (n = 10 590), 66.6 % and 69.6 % for cranial CT (n = 16 098) and 37.8 % and 44.0 % for chest CT (n = 10 387) of the compared national DRLs, respectively. Overall, the CTDI vol exceeded national DRLs in 1.9 % of the examinations, while the DLP exceeded national DRLs in 2.9 % of the examinations. Betw...

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“…The small sample size (n = 7 in subgroup 1 and n = 5 in subgroup 2) was an important limitation of our study, and it might have an effect on the statistical outcome. Taylor et al 32 reported a high variability of dose descriptors in their study using the data of 3805 chest CT collected by an automated exposure control system. They recommended the inclusion of greater than 190 patients to decrease the confidence interval to 10% of the median value if analyzing dose descriptors.…”

Section: Discussionmentioning

confidence: 99%

Submillisievert Computed Tomography of the Chest Using Model-Based Iterative Algorithm

Deák

Maertz

Meurer

et al. 2017

Journal of Computer Assisted Tomography

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CT dose survey in adults: what sample size for what precision?

Cited by 27 publications

References 13 publications

Task-based quantification of image quality using a model observer in abdominal CT: a multicentre study

Task-based quantification of image quality using a model observer in abdominal CT: a multicentre study

Cloud-Based CT Dose Monitoring using the DICOM-Structured Report: Fully Automated Analysis in Regard to National Diagnostic Reference Levels

Submillisievert Computed Tomography of the Chest Using Model-Based Iterative Algorithm

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