Digital Radiography Reject Analysis: Data Collection Methodology, Results, and Recommendations from an In-depth Investigation at Two Hospitals

Abstract

Reject analysis was performed on 288,000 computed radiography (CR) image records collected from a university hospital (UH) and a large community hospital (CH). Each record contains image information, such as body part and view position, exposure level, technologist identifier, and--if the image was rejected--the reason for rejection. Extensive database filtering was required to ensure the integrity of the reject-rate calculations. The reject rate for CR across all departments and across all exam types was 4.4% at UH and 4.9% at CH. The most frequently occurring exam types with reject rates of 8% or greater were found to be common to both institutions (skull/facial bones, shoulder, hip, spines, in-department chest, pelvis). Positioning errors and anatomy cutoff were the most frequently occurring reasons for rejection, accounting for 45% of rejects at CH and 56% at UH. Improper exposure was the next most frequently occurring reject reason (14% of rejects at CH and 13% at UH), followed by patient motion (11% of rejects at CH and 7% at UH). Chest exams were the most frequently performed exam at both institutions (26% at UH and 45% at CH) with half captured in-department and half captured using portable x-ray equipment. A ninefold greater reject rate was found for in-department (9%) versus portable chest exams (1%). Problems identified with the integrity of the data used for reject analysis can be mitigated in the future by objectifying quality assurance (QA) procedures and by standardizing the nomenclature and definitions for QA deficiencies.

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

“…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].…”

Sharpness and noise in digital chest radiographs, assessed by visual rating

“…However, this retakes alone may not indicate overall improved image quality and optimal radiation dose. An acceptable level of contrast and brightness can be obtained through post-processing, even with a noisy image (Foos et al, 2009). It has been proposed that the two biggest quality errors in computed radiography (CR) are related to poor positioning and noisy images.…”

“…However, Wu et al (2008), found that retakes with CR were similar to conventional filmscreen techniques; tumours ignored were sized 10-32mm, and 63% of the tumours were carcinomas. A reject analysis of 288.000 CR image records was published by Foos et al, 2009. Of these images collected from two large hospitals whereas chest exams were the most frequently performed examination at both institutions; positioning errors and anatomy cut-offs, followed by improper exposure were reasons for the most frequently occurring overall rejects.…”

“…Noise could also remain in the image due to a lack of noise reduction in the CR plate reader. Foos et al (2009) highlighted the feedback from rejecting images to ensure better acquiring.…”

Evaluation of digital radiography practice using exposure index tracking

“…The ability to identify outliers in practice allows for targeted interventions. There are examples of QC programs using automated collection methods to identify trends in computed tomography (CT), 14 , 15 and in CR tracking rejects and exposure levels over time 16 , 17 , 18 , 19 . AAPM Task Group 151 (6) discusses the appropriate quality assurance elements in a digital radiography program, including analysis of exposure levels.…”