One Year’s Results from a Server-Based System for Performing Reject Analysis and Exposure Analysis in Computed Radiography

Jones, A. Kyle; Polman, Raimund; Willis, Charles E.; Shepard, S. Jeff

doi:10.1007/s10278-009-9236-2

Cited by 46 publications

(61 citation statements)

References 14 publications

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“…Reject analysis is an important component of quality assurance programs for medical imaging departments. It forms a basis for determining the causes of rejected images and helps guide radiographer training, department workflow and ultimately reduces patient dose . The objective of a radiographic examination was to acquire diagnostic images in at least two planes to help diagnose conditions or injuries while minimising the exposure to ionising radiation that the patient receives.…”

Section: Introductionmentioning

confidence: 99%

Reject rate analysis in digital radiography: an Australian emergency imaging department case study

Atkinson

Neep

Starkey

2019

J of Medical Radiation Sci

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Introduction Reject analysis in digital radiography (DR) helps guide the education and training of staff, influences department workflow, reduces patient dose and improves department efficiency. The purpose of this study was to investigate rejected radiographs at a major metropolitan emergency imaging department to help form a benchmark of reject rates for DR and to assess what radiographs are being rejected and why. Methods A retrospective longitudinal study was undertaken as an in‐depth clinical audit. The data were collected using automated reject analysis software from two digital x‐ray systems from June 2015 to April 2017. The overall reject rate, reasons for rejection as well as the reject rates for individual radiographers, examination types and projections were analysed. Results A total of 90,298 radiographic images were acquired and included in the analysis. The average reject rate was 9%, and the most frequent reasons for image rejection were positioning error (49%) and anatomy cut‐off (21%). The reject rate varied between radiographers as well as for individual examination types and projections. Conclusions The variation in radiographer reject rates and the high reject rate for some projections indicate that reject analysis is still necessary as a quality assurance tool for DR. A feedback system between radiologists and radiographers may reduce the high percentage of positioning errors by standardising the technical factors used to assess image quality. Future reject analysis should be conducted regularly incorporating an exposure indicator analysis as well as retrospective assessment of individual rejected images.

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Section: Introductionmentioning

confidence: 99%

Reject rate analysis in digital radiography: an Australian emergency imaging department case study

Atkinson

Neep

Starkey

2019

J of Medical Radiation Sci

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“…Reject rates, which are defined as the total number of rejected images divided by the total number of images acquired over an established time period, are used by most clinical sites as an integral component of an overall QA program. Reject rates for sites using digital radiography systems are reported to range between 3 % and 10 % [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Variability in the Assessment of Digital Chest X-ray Image Quality

et al. 2012

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A large database of digital chest radiographs was developed over a 14-month period. Ten radiographic technologists and five radiologists independently evaluated a stratified subset of images from the database for quality deficiencies and decided whether each image should be rejected. The evaluation results showed that the radiographic technologists and radiologists agreed only moderately in their assessments. When compared against each other, radiologist and technologist reader groups were found to have even less agreement than the inter-reader agreement within each group. Radiologists were found to be more accepting of limitedquality studies than technologists. Evidence from the study suggests that the technologists weighted their reject decisions more heavily on objective technical attributes, while the radiologists weighted their decisions more heavily on diagnostic interpretability relative to the image indication. A suite of reject-detection algorithms was independently run on the images in the database. The algorithms detected 4 % of postero-anterior chest exams that were accepted by the technologist who originally captured the image but which would have been rejected by the technologist peer group. When algorithm results were made available to the technologists during the study, there was no improvement in inter-reader agreement in deciding whether to reject an image. The algorithm results do, however, provide new quality information that could be captured within a site-wide, reject-tracking database and leveraged as part of a site-wide QA program.

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“…Images were sorted on the respective reasons for rejection. Here, we gave a more detailed classification than before [9].…”

Section: Radiographic Proceduresmentioning

confidence: 98%

Optimization of the Radiological Protection of Patients Undergoing Digital Radiography

Zhang¹,

Chu

2011

J Digit Imaging

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Because of a much higher dynamic range of flat panel detectors, patient dose can vary without change of image quality being perceived by radiologists. This condition makes optimization (OT) of radiation protection undergoing digital radiography (DR) more complex, while a chance to reduced patient dose also exists. In this study, we evaluated the difference of patient radiation and image rejection before and after OT to identify if it is necessary to carry out an OT procedure in a routine task with DR. The study consisted of a measurement of the dose area product (DAP) and entrance surface dose (ESD) received by a reference group of patients for eight common radiographic procedures using the DR system before and after OT. Meanwhile image rejection data during two 2-month periods were collected and sorted according to reason. For every radiographic procedure, t tests showed significant difference in average ESD and DAP before and after OT (p < 0.005). The ESDs from most examinations before OT were three times higher than that after OT. For DAPs, the difference is more significant. Image rejection rate after OT is significantly lower than that before OT (χ (2) = 36.5, p < 0.005). The substantial reductions of dose after OT resulted from appropriate mAs and exposure field. For DR patient dose, less than recommended diagnostic reference level can meet quality criteria and clinic diagnosis.

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One Year’s Results from a Server-Based System for Performing Reject Analysis and Exposure Analysis in Computed Radiography

Cited by 46 publications

References 14 publications

Reject rate analysis in digital radiography: an Australian emergency imaging department case study

Reject rate analysis in digital radiography: an Australian emergency imaging department case study

Investigation of the Variability in the Assessment of Digital Chest X-ray Image Quality

Optimization of the Radiological Protection of Patients Undergoing Digital Radiography

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