AAPM task group report 305: Guidance for standardization of vendor‐neutral reject analysis in radiography

Little, Kevin J.; Reiser, Ingrid; Apgar, Bruce; Dalal, Poonam; Dave, Jaydev K.; Fisher, R; Hulme, Katie; Jafari, Mary Ellen; Marshall, Emily L.; Stephen, Meyer,; Moore, Quentin T; Nicole, Murphy,; Nishino, Thomas; Nye, Katelyn; O'Donnell, Kevin; Sabol, John M.; Sánchez, Adrian A.; Sensakovic, William F.; Tarbox, Lawrence; Uzenoff, Robert; Walz-Flannigan, Alisa; Willis, Charles E.; Zhang, Jie

doi:10.1002/acm2.13938

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…In our department a designated radiologist trained in radioprotection oversees quality control using the Intuitus DACS software to monitor reject rates related to each technologist. This is carried under the scope of personal performance improvement goals to avoid anxiety of negative repercussions and hiding rejects [20]. Keeping the rejected acquisitions in the PACS is not necessary for calculating reject rates but it can be useful if one technologist is associated with high reject rate to review the rejection appropriateness concerning these rejected images.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…To improve reject analysis, monitoring solutions could be modified on DICOM, PACS or DACS for simple access to reject rates [20]. The Intuitus DACS software used by our department accelerated and facilitated this reject analysis.…”

Section: Discussionmentioning

confidence: 99%

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

Haddad,

Saleme,

Howarth

et al. 2023

JBSR

View full text Add to dashboard Cite

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AAPM task group report 305: Guidance for standardization of vendor‐neutral reject analysis in radiography

Little

Reiser

Apgar

et al. 2023

J Applied Clin Med Phys

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Reject rate analysis is considered an integral part of a diagnostic radiography quality control (QC) program. A rejected image is a patient radiograph that was not presented to a radiologist for diagnosis and that contributes unnecessary radiation dose to the patient. Reject rates that are either too high or too low may suggest systemic department shortcomings in QC mechanisms. Due to the lack Kevin Little and Ingrid Reiser contributed equally to this report.

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Multi‐centre digital radiography reject analysis for different clinical room use types: The establishment of local reject reference levels for public hospital departments

Serra,

Neep,

Ryan

2024

J of Medical Radiation Sci

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IntroductionReject analysis in digital radiography helps guide the training of staff to reduce patient radiation dose and improve department efficiency. The purpose of this study was to perform a multi‐centre, vendor agnostic reject analysis across different room usage types, and to provide benchmarks for comparison.MethodsRetrospective reject and exposure log data were collected via USB from fixed general X‐ray systems across multiple Australian sites, for collation and analysis. The overall reject rate, local reject reference level, absolute and relative reject rates for body part categories, reject rates by room usage types and the reject rate for each reason of rejection were calculated.ResultsData were collected from 44 X‐ray systems, across 11 hospitals. A total of 2,031,713 acquired images and 172,495 rejected images were included. The median reject rate was 9.1%. The local reject reference level (LRRL), set as the 75th percentile of all reject rates, was 10.6%. Median reject rates by room type were emergency (7.4%), inpatients + outpatients (9.6%), outpatients (9.2%), and hybrid (10.1%). The highest absolute reject rates by body part were chest (2.1%) and knee (1.4%). The highest relative rates by body part were knee (18.1%) and pelvis (17.2%). The most frequent reasons for image rejection were patient positioning (76%) and patient motion (7.5%).ConclusionsThe results compare well with previously published data. The range of reject rates highlights the need to analyse typical reject rates in different ways. With analysis feedback to participating sites and the implementation of standardised reject reasons, future analysis should monitor whether reject rates reduce.

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AAPM task group report 305: Guidance for standardization of vendor‐neutral reject analysis in radiography

Cited by 3 publications

References 30 publications

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

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

AAPM task group report 305: Guidance for standardization of vendor‐neutral reject analysis in radiography

Multi‐centre digital radiography reject analysis for different clinical room use types: The establishment of local reject reference levels for public hospital departments

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