ArticlePDF AvailableLiterature Review

Image rejects/retakes-radiographic challenges

Authors:
  • Norwegian University of Science and Technology, Gjøvik, Norway
  • Norwegian University of Science and Technology, Gjøvik, Norway

Abstract

A general held position among radiological personnel prior to digitalisation was that the problem of image rejects/retakes should more or less vanish. However, rejects/retakes still impose several challenges within radiographic imaging; they occupy unnecessary resources, expose patients to unnecessary ionizing radiation and may also indicate suboptimal quality management. The latter is the main objective of this paper, which is based on a survey of international papers published both for screen/film and digital technology. The digital revolution in imaging seems to have reduced the percentage of image rejects/retakes from 10-15 to 3-5 %. The major contribution to the decrease appears to be the dramatic reduction of incorrect exposures. At the same time, rejects/retakes due to lack of operator competence (positioning, etc.) are almost unchanged, or perhaps slightly increased (due to lack of proper technical competence, incorrect organ coding, etc.). However, the causes of rejects/retakes are in many cases defined and reported with reference to radiographers' subjective evaluations. Thus, unless radiographers share common views on image quality and acceptance criteria, objective measurements and assessments of reject/retake rates are challenging tasks. Interestingly, none of the investigated papers employs image quality parameters such as 'too much noise' as categories for rejects/retakes. Surprisingly, no reject/retake analysis seems yet to have been conducted for direct digital radiography departments. An increased percentage of rejects/retakes is related to 'digital skills' of radiographers and therefore points to areas for extended education and training. Furthermore, there is a need to investigate the inter-subjectivity of radiographers' perception of, and attitude towards, both technical and clinical image quality criteria. Finally, there may be a need to validate whether reject/retake rate analysis is such an effective quality indicator as has been asserted.
IMAGE REJECTS/RETAKES—RADIOGRAPHIC CHALLENGES
D. Waaler* and B. Hofmann
Department of Health, Care and Nursing, Gjøvik University College, Gjøvik, Norway
*Corresponding author: dag.waaler@hig.no
A general held position among radiological personnel prior to digitalisation was that the problem of image rejects/retakes
should more or less vanish. However, rejects/retakes still impose several challenges within radiographic imaging; they occupy
unnecessary resources, expose patients to unnecessary ionizing radiation and may also indicate suboptimal quality manage-
ment. The latter is the main objective of this paper, which is based on a survey of international papers published both for
screen/film and digital technology. The digital revolution in imaging seems to have reduced the percentage of image rejects/
retakes from 1015 to 35 %. The major contribution to the decrease appears to be the dramatic reduction of incorrect
exposures. At the same time, rejects/retakes due to lack of operator competence (positioning, etc.) are almost unchanged, or
perhaps slightly increased (due to lack of proper technical competence, incorrect organ coding, etc.). However, the causes of
rejects/retakes are in many cases defined and reported with reference to radiographers’ subjective evaluations. Thus, unless
radiographers share common views on image quality and acceptance criteria, objective measurements and assessments of
reject/retake rates are challenging tasks. Interestingly, none of the investigated papers employs image quality parameters such
as ‘too much noise’ as categories for rejects/retakes. Surprisingly, no reject/retake analysis seems yet to have been conducted
for direct digital radiography departments. An increased percentage of rejects/retakes is related to ‘digital skills’ of radiogra-
phers and therefore points to areas for extended education and training. Furthermore, there is a need to investigate the inter-
subjectivity of radiographers’ perception of, and attitude towards, both technical and clinical image quality criteria. Finally,
there may be a need to validate whether reject/retake rate analysis is such an effective quality indicator as has been asserted.
INTRODUCTION
Rejects and subsequent retakes of diagnostic X-ray
images impose important challenges within radio-
graphic imaging. They expose patients to unnecess-
ary ionizing radiation and added inconveniences
(1)
,
occupy unnecessary materials and personal
resources
(2,3)
and may also indicate suboptimal
quality management. Reject/retake rates for film-
based departments have been documented to be
between 10 and 15 %
(2 – 4)
, and a major contribution
of these could be attributed to the tendency of
making incorrect exposures due to limited dynamic
range of screen/film systems. Thus the digitalisation
of medical imaging created expectations that the
problem of image retakes should disappear
(5)
.
Several research papers, however, report reject/
retake rates in digital departments still as high as
5 % and even higher
(6 – 10)
. So the following question
persists: why is the problem of image rejects/retakes
not eliminated?
DIGITAL ABOLITION OF IMAGE
REJECTS/RETAKES?
Reject/retake analysis is a long-established method
of quality control in diagnostic radiology
(9,11)
.At
the same time, it must be stressed that reject/retake
analysis is but one method among others for asses-
sing quality levels
(4,12 – 14)
and that the real task cer-
tainly is to identify the causes of rejects/retakes and
to reduce or eliminate these
(14)
. This is particularly
true when causes are difficult to control, as, for
instance, in relation to patient variability, equipment
quality and workplace cultures.
There are also other reasons why reject/retake
analytical studies should be subject to critical scru-
tiny. First of all, there are no explicit criteria for how
to count image rejects/retakes for digital
systems
(10,12)
. Initially one might try to define a
rejected image by one that ‘does not add diagnostic
information to the clinical analysis because of poor
image quality’
(15)
. In practice, however, image
quality will be based on the radiographer’s subjective
judgements
(6 – 10,12,15,16)
. In a screen/film world,
rejected images could be measured in terms of the
number of wasted films (physically collected in a
waste bin and routinely counted), whereas in the
digital world, they are often defined in terms of
deleted images
(9)
. The lack of both proper procedures
and encompassing analytical software
(9,17)
makes the
term ‘image reject/retake’ even more complex.
Additionally, there are reasons to believe that the
‘digitally offered’ practical ease of taking additional
images has also resulted in an increased number of
repeated examinations
(10)
. By reporting retake/reject
rates as percentages, one also tends to hide the fact
that the absolute number of retakes increases as the
overall number of images increases. Finally, as the
most important cause of rejects/retakes in digital
departments is positioning error, and as the prob-
ability of improper positioning obviously increases
for larger body parts (chest, skull/facial bones,
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shoulder, hip, spine and pelvis)
(10)
, the effect of
rejects/retakes in terms of increased radiation doses
to the patients will be even higher than the percen-
tage in numbers of rejected/retaken images should
indicate.
Although reject/retake analysis might have some
limitations and even be impossible, it is generally
believed that it is a valuable radiographic QA
tool
(18,19)
. Not least for its fruitful contribution to
(a) unveiling staff competence gaps, (b) planning of
educational strategies
(10)
, (c) keeping track of radi-
ation doses to patients
(9)
, (d) evaluating old and
developing new examination protocols and work
procedures, as well as (e) assessing the overall level
of image quality.
One reason why initial studies of digital systems
showed reduced rates
(8)
was that some systems did
not allow image deletion
(9)
and that the number of
actual retakes thus turned out quite few. Standard
software for repeat analysis in PACS will underesti-
mate the real retake rate because it is based on
images deleted in the PACS and ignores images
deleted at the modality.
Surprisingly there are no published papers report-
ing rejects/retakes in direct radiography (DR)
departments, thus leaving the question of if,orhow,
rejects/retakes in DR might differ from computed
radiography (CR, i.e. phosphor plate technology)
still unsettled. A recent study carried out in a local
community hospital in Norway using DR found dis-
turbing 12.5 % reject/retake rates
(20)
. Such findings
call for an urgent need to investigate DR reject/
retake rates. A possible explanation for increased
retakes/rejects in DR systems is the increased ease,
and temptation, of ‘retaking an image just to be
sure’.
REASONS FOR RETAKES
All studies identified positioning errors to have
moved from being the second most dominating
cause of rejects/retakes to be the overall dominating
one
(6 – 10)
upon introduction of digital systems, and
one study also indicates that positioning errors in
absolute numbers have slightly increased
(9)
. At the
same time, the number of rejects/retakes attributed
to patient breathing, motion, etc. has remained
almost constant
(6 – 10)
. The major change observed in
reject/retake rates after digitalisation is, of course,
the substantial reduction (5- to 6-folds) in exposure
errors (over- and underexposure)
(6 – 9)
thanks to the
increased dynamic range offered by digital systems.
While constituting in the order of 40 60 %
(6–9)
of
all rejects/retakes in screen/film systems, the relative
contribution of exposure errors in digital systems
has been reduced to 10– 15 %
(6 – 10)
. The incorrect
exposures remaining in digital (CR) departments are
mainly due to incorrect estimation of patient size,
particularly in combination with manual exposure
settings
(9)
, but also for automated setting situations
in combination with incorrect positioning. As for
reject/retake rates in relation to examination types,
thorax examinations counted for over half of the
number of image rejects/retakes in two of the sur-
veyed studies
(6,8)
, and dominating in a third
(9)
,
whereas examinations of skull/facial bones,
shoulders, hip, spine, pelvis contributed the most in
a fourth one
(10)
. One study also suggested that the
introduction of PACS did not only fail to reduce
reject/retake rates, but also generated new types,
such as the incorrect choice of algorithm
(6)
.
Additionally, rejects/retakes seem to be slightly over-
represented during mornings and in weekends
(8)
.
Some slight differences between local hospitals and
university hospitals have been documented
(10)
and,
interestingly, major differences have been reported
between in-department (9 %) versus portable chest
exams (1 %)
(10)
.
DISCUSSION
Overall, there are surprisingly few published reject/
retake analytical studies in the digital period and no
one for DR. People may have been too confident
that the problem of rejects/retakes in a digital
environment would be eliminated, and considered
new studies to be ‘unnecessary’. Alternatively, the
reason might be that the ease of rejecting and retak-
ing images, combined with the ‘invisibility’ of elec-
tronically deleted images (as opposed to the
physically waste film bin), rendered the problem out
of sight and out of mind.
In searching for unnecessary exposures, an esti-
mation of rejects/retakes on the basis of the number
of deleted digital images may result in a significant
underestimation simply because the original images,
which are taken in order to obtain better results, in
fact are not deleted. Reasons for not deleting images
may be that it is not necessary, forgetfulness or even
that the old image in the end showed up to be better
than the new one. Moreover, there are indications
that the ‘all-agreed’ historic references to high
reject/retake rates in screen/film systems are obso-
lete or overestimated. In fact some older studies did
report lower reject/retake rates in screen/film
departments (below 8 %)
(21,22)
and, interestingly,
studies from Pakistan and China report substantially
lower reject/retake rates for both screen/film and
digital departments
(23,24)
. Table 1 shows an overview
of the results from some of the studies in our survey.
So, even if the results show large variability, there
seems to be little doubt that the transition from
screen/film to digital radiography has led to a sub-
stantial reduction in the technical reasons for image
rejects/retakes (exposure errors). There are, however,
reasons to assert that rejects/retakes have increased
D. WAALER AND B. HOFMANN
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for reasons related to non-technical reasons, mainly
due to skills of the radiographer. There may be
several possible explanations for this. A simple one
is the disappearance of the workload barrier experi-
enced in screen/film systems by the additional time
spent ‘taking one more image just to be sure’.
Another explanation is that radiographers have
become too self-confident or too reliant on
advanced technology
(9)
. A profoundly challenging
explanation is the effect of the disappearance of the
image quality discussions between radiographers
and radiologists that used to take place at the light
board, which might leave the radiographers in some
uncertainty as to whether the image quality criteria
expected by the radiologist
(25)
are fulfilled.
A striking fact about all the reject/retake analyti-
cal studies investigated here is that neither of them
have categories for image rejects/retakes with refer-
ence to unacceptable (quantum) noise and/or impro-
per spatial resolution errors. One reason for this may
be that the examination protocols are developed and
optimised using contrast-detail (CD) phantoms and
thus that image quality of the procedure is supposed
to be quantum noise-limited. Ma
˚nsson et al.
(26)
suggests, however, that the quality of projection X-
ray examinations most frequently are limited by ana-
tomical ‘noise’, not quantum noise. If this is true,
the patient radiation dose can be reduced without
significant reduction in diagnostic accuracy
(26)
.If,
on the other hand, imaging protocols were optimised
on the assumption of anatomic ‘noise’ as the limit-
ing quality factor, one would also expect occasional
rejects/retakes because of quantum noise. The
apparent absence of rejects/retakes attributed to
quantum noise thus may indicate exposed radiation
dose levels to be above optimal values.
A number of factors, both technical and anatom-
ical, are involved in delivering images that are good
enough for a given diagnostic purpose with the lowest
possible radiation dose. Image perception and obser-
ver performance are also important factors
(26 – 29)
.
This implies that the list of reasons to rejects/retakes
should be expanded. The same applies to the list of
possible measures for image optimisation to avoid
rejects/retakes. The increasing impact of image post-
processing techniques, including adaptive multi-
frequency processing, noise reduction and contrast
enhancement
(30,31)
, also necessitates focus on how to
utilise these possibilities to reduce radiation doses to
the patients. At the same time, there seems to be
important not to ignore the fundamental challenges
of diagnostic uncertainty in image-based technol-
ogies
(32)
, for example, in (prospectively) assessing
diagnostic utility.
This review is based on searches in the scientific
literature and may, of course, have overlooked paper
reports and articles in national publications without
English abstracts. The relatively low number of
identified articles clearly indicates that more research
in the field is needed.
Table 1. Total number of exposures (n), reject/retake rates (%) and percentage distribution of causes for rejects/retakes in
conventional and digital (CR) departments taken from some representative reject/retake analytical studies performed during
the analogue-to-digital transition of radiology departments.
Study Conventional (screen/film) Digital (CR)
nReject/
retake
rate
(%)
Reject/retake causes (%) nReject/
retake
rate
(%)
Reject/retake causes (%)
Positioning Exposure Other Positioning Exposure Other
Weatherburn
et al.
(6)
5791 9.9 44 32 23 6597 7.3 83 2 15
Peer et al.
(7)
5233 15.6 28 63 9 16 791 2.3 81 7 12
Honea
et al.
(8)
160 621 4.1 62 10 28
Lau et al.
(24)
19 155 2.1 28 39 33 17 042 1.3 55 8 37
Al-Malki
et al.
(22)
8887 7.9 23 62 15
Nol et al.
(9)
3187 10.5 24 41 17 3252 4.7 60 16 12
Waseem
et al.
(23)
170 300 5.5 25 67 8 174 550 0.8 30 54 16
Foos
et al.
(10)
274 840 4.8 51 13 36
Although some papers report causes in more detailed bins, they were, in order to be able to compare, grouped into
positioning, exposure (both over- and under-) and other. The disparities of the studies unfortunately do not allow proper
meta-analysis.
IMAGE REJECTS/RETAKES
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CONCLUSION
To measure rejects/retakes in an objective manner is
a challenging task. Even with specially developed
software, it is difficult to obtain good estimates, as
the choice of a reject/retake depends on radiogra-
pher’s subjective reviews. It is, therefore, important
to provide knowledge about how radiographer’s
norms and standards about image quality are
formed.
Rejects/retakes are not eliminated with the intro-
duction of digital radiography, but their causes have
changed significantly. Rejects/retakes for the screen/
film systems, to a large extent, are exposure-related,
but in digital systems, they are mainly related to (the
lack of) radiographer’s skills, particularly to patient
positioning and proper equipment operations.
Summing up, important current challenges for
radiography practice and radiographers are:
(1) investigate the inter-subjectivity of radiogra-
phers’ perception of, and attitude towards, both
clinical and technical image quality criteria;
(2) develop training and educational strategies to
enhance the understanding and working compe-
tence of digital image quality among
radiographers;
(3) reconsider reject/retake analytical concepts and
methods towards more adequate measures of
unnecessary exposure;
(4) investigate whether the concept of (new)exposure
that is not used in the diagnostic process to be a
more suitable target for considering unnecessary
exposure than rejects/retakes;
(5) develop proper software systems for reject/retake
recording with intuitive operation and workflow;
(6) compare rejects/retakes in direct (DR) and
phosphor plate (CR) digital imaging.
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IMAGE REJECTS/RETAKES
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... The quality of a medical image directly affects the diagnosis and treatment of the disease. Hence, a set of medical image quality standards have been established by experts based on domestic and foreign long-term clinical tests (11,12). Poor radiographic operation skills, due to a lack of education and experience, result in poor quality DR images in clinical practice (9). ...
... For example, some studies (29,30) have improved the quality of thoracic CT examinations by providing patients with breathing training. The issues raised by Waaler and Hofmann (12) regarding the rejection and duplication of diagnostic x-ray images pose new challenges to radiographic imaging. The quality control process involves key personnel in the imaging department, including the radiologist, radiologic technologist, and qualified medical physicist. ...
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Purpose: To standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard. Materials and methods: A dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively). The quality control standard for lumbar spine x-ray radiography in this study was defined using textbook guidelines of as a reference. An enhanced encoder-decoder fully convolutional network with U-net as the backbone was implemented to segment the anatomical structures in the x-ray images. The segmentations were used to build an automatic assessment method to detect unqualified images. The dice similarity coefficient was used to evaluate segmentation performance. Results: The dice similarity coefficient of the anteroposterior position images ranged from 0.82 to 0.96 (mean 0.91 ± 0.06); the dice similarity coefficient of the lateral position images ranged from 0.71 to 0.95 (mean 0.87 ± 0.10); the dice similarity coefficient of the oblique position images ranged from 0.66 to 0.93 (mean 0.80 ± 0.14). The accuracy, sensitivity, and specificity of the assessment method on the validation set were 0.971-0.990 (mean 0.98 ± 0.10), 0.714-0.933 (mean 0.86 ± 0.13), and 0.995-1.000 (mean 0.99 ± 0.12) for the three positions, respectively. Conclusion: This deep learning-based algorithm achieves accurate segmentation of lumbar spine x-ray images. It provides a reliable and efficient method to identify the shape of the lumbar spine while automatically determining the radiographic image quality.
... Several factors are reported to influence rejects of imaging among radiographers including skills and the inter-subjectivity in assessment, and attitude towards, both technical and clinical image quality criteria [12,15]. Waaler and Hofmann [15] highlight inter-subjectivity of radiographers' perception of image quality to be a challenge in the effort to reduce reject rate. ...
... Several factors are reported to influence rejects of imaging among radiographers including skills and the inter-subjectivity in assessment, and attitude towards, both technical and clinical image quality criteria [12,15]. Waaler and Hofmann [15] highlight inter-subjectivity of radiographers' perception of image quality to be a challenge in the effort to reduce reject rate. Having higher years of work experience is reported to have some influence on how radiographers assess images, with more experienced radiographers assessing more than merely radiographic imaging criteria [16]. ...
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Background Assessing the quality of diagnostic images is subjective and influenced by factors such education, skills, and experience of the assessor. This study aims to explore the radiographers’ assessments of medical usefulness or rejection of X-ray images in specific cases. Results Eighty-one radiographers from different countries responded to the questionnaire distributed online at the EFRS research HUB at ECR 2020 (a 15% response rate). Forty-two percent of the respondents practiced in the UK and Ireland. In addition to rejecting or keeping images in the presented 30 cases and giving a main reason for the images rejected, the participants explained their choice using comments, 1176 comments were obtained. Sixty percent of the comments were on kept images. The respondents kept on average 63% of the images. In the “Keep”, “Could keep”, and “Reject” categories on average 84%, 63% and 43% of images were kept respectively. The most common reasons given for rejecting an image were suboptimal positioning and centering. Potential diagnostic value and radiation protection were indicated as reasons to keep an image perceived as of low quality reported in n = 353 and n = 33 comments respectively. Conclusions There is an agreement internationally on what makes a good quality X-ray image. However, the opinion on medical usefulness of images of low or poor quality compared to image criteria varies. Diagnostic capability and radiation protection was the rationale used for keeping images not fulfilling image criteria. There seems to be a need for diagnostic quality to be included in image assessment in clinical practice.
... The era of conventional radiography almost saw the retirement of reject analysis, with the concept of digital radiography limiting the need for it. 6,7 Previously, the most significant reason for rejection in conventional radiography (CR) was under or over exposure. [6][7][8] Digital radiography manufacturers emphasised that the evolution X-ray equipment would significantly reduce the need for reject analysis since under or over exposure in DR is considerably more forgiving. ...
... 6,7 Previously, the most significant reason for rejection in conventional radiography (CR) was under or over exposure. [6][7][8] Digital radiography manufacturers emphasised that the evolution X-ray equipment would significantly reduce the need for reject analysis since under or over exposure in DR is considerably more forgiving. 6 Despite this, it has been proposed that a spike in reject rates after the changeover from CR to DR is attributed to it being easier than ever to obtain and discard radiographs. ...
Article
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Introduction The largest source of manmade ionising radiation exposure to the public stems from diagnostic medical imaging examinations. Reject analysis, a form of quality assurance, was introduced to minimise repeat exposures. The purpose of this study was to analyse projection‐specific reject rates and radiographic examinations with multiple rejects. Methods A retrospective audit of rejected radiographs was undertaken in a busy Australian metropolitan emergency digital X‐ray room from March to June 2018. The data were collected by reject analysis software embedded within the X‐ray unit. Reject rates, and reasons for rejection for each X‐ray projection were analysed. Results Data from 11, 596 images showed overall reject rate was 10.3% and the overall multiple reject rate was 1.3%. The projections with both a high number and high percentage of rejects were antero‐posterior (AP) chest (175, 18.1%), AP pelvis (78, 22.5%), horizontal beam hip (61, 33.5%) and horizontal beam knee (116, 30.5%). The projections with both a high frequency and multiple reject rate were horizontal beam knee (32, 8.4%) and horizontal beam hip (17, 9.3%). The top reasons for multiple rejects were positioning (67.1%) and anatomy cut‐off (8.4%). Conclusions The findings of this study demonstrated that projection‐specific reject and multiple reject analysis in digital radiography is necessary in identifying areas for quality improvement which will reduce radiation exposure to patients. Projections that were frequently repeated in this study were horizontal beam knee and horizontal beam hip. Future research could involve re‐auditing the department following the implementation of improvement strategies to reduce unnecessary radiation exposure.
... Moreover, the radiographer needs to determine the appropriate moment to successfully perform an exposure, which can avoid image retakes caused by the motion artifacts and the position errors [6], [7]. Avoiding image retakes is crucial to reducing unnecessary radiation dose and inconvenience of the patient, as well as in avoiding waste of medical resources for hospitals [8]- [10]. In X-ray imaging, the two key procedures including recognizing the exposure moment and the exposure region tend to require costly and error-prone manual involvement. ...
Article
The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique, which can decrease the heavy workload of the radiographers, optimize imaging workflow and improve image quality. However, there is little related research and practice in X-ray imaging. In this paper, we focus on two key problems in X-ray imaging automation: automatic recognition of the exposure moment and the exposure region. Consequently, we propose an automatic video analysis framework based on the hybrid model, approaching real-time performance. The framework consists of three interdependent components: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to obtain the corresponding body keypoints and body Bboxes. Combining and analyzing the two different types of body structure representations is to obtain rich spatial location information about the patient body structure. Motion State Tracing focuses on the motion state analysis of the exposure region to recognize the appropriate exposure moment. The exposure region is calculated by Body Modeling when the exposure moment appears. A large-scale dataset for X-ray examination scene is built to validate the performance of the proposed method. Extensive experiments demonstrate the superiority of the proposed method in automatically recognizing the exposure moment and exposure region. This paradigm provides the first method that can enable automatically and accurately recognize the exposure region in X-ray imaging without the help of the radiographer.
... One important type of waste in imaging is deleted images, retakes, rejected images and images without diagnostic value. While it was predicted that retakes would drastically reduce when digital images replaced films (due to improved image production), this has not occurred [29,30]. Retakes and rejects are reported to be at the same level (5-12%) now as when films were used [31][32][33][34][35][36][37][38][39]. ...
Article
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There is extensive waste in diagnostic imaging, at the same time as there are long waiting lists. While the problem of waste in diagnostics has been known for a long time, the problem persists. Accordingly, the objective of this study is to investigate various types of waste in imaging and why they are so pervasive and persistent in today’s health services. After a short overview of different conceptions and types of waste in diagnostic imaging (in radiology), we identify two reasons why these types of waste are so difficult to address: (1) they are invisible in the healthcare system and (2) wasteful imaging is driven by strong external forces and internal drivers. Lastly, we present specific measures to address wasteful imaging. Visualizing and identifying the waste in diagnostic imaging and its ingrained drivers is one important way to improve the quality and efficiency of healthcare services.
... Other research highlighted the necessity for continuous image quality control (11,12) in order to ensure proper image quality. The most common finding of the past studies revealed that collimation is often not performed according to the international guidelines (13)(14)(15)(16) and the main challenge is operator skill (17). Since the introduction of Picture Archiving and Communication System (PACS) the interaction and communication between radiographers and radiologists have been significantly reduced (18), culminating in tele-radiological services. ...
Preprint
Purpose To implement a tool for real time image quality feedback for chest radiographs into the clinical routine and to evaluate the effect of the system on the image quality of the acquired radiographs. Materials and Methods A real time Artificial Intelligence (AI) image quality feedback tool is developed that analyzes chest PA x-rays right after the completion of the examination at the x-ray system and provides visual feedback to the system operator with respect to adherence to desired standards of collimation, patient rotation and inspiration. In order to track image quality changes over time, results were compared to image quality assessment for images, acquired prior to system implementation. Results Compared to the image quality prior to the installation of the real time image quality feedback solution, it is shown that a relative increase of images with optimal image quality with respect to collimation, patient rotation and inspiration is achieved by 30% (p<0.01). A relative improvement of 28% (p<0.01) is observed for the increase of images with optimal collimation, followed by a relative increase of 4% (p<0.01) of images with optimal inspiration. Finally, a detailed analysis is presented that shows that the average unnecessarily exposed area is reduced by 34% (p<0.01). Discussion This study shows that it is possible to significantly improve image quality using a real time AI-based image quality feedback tool. The developed tool not only provides objective and impartial criticism and helps x-ray operators identify areas for improvement, but also gives positive feedback.
... The rejection rate in screen film radiography and then computed radiography were believed to be higher in number perhaps due to too much manual interventions and also the narrow exposure latitude of film/screen results in the high rejection rates for under/over exposed films. On the other hand, digital systems were assumed to have reduced the rejection due to more forgiving exposure latitude [6]. In addition to the wider exposure latitude, the advantages of digital over analog systems include processing capabilities and manipulation, which allow technologists to adjust radiographs quality [7]. ...
Article
Full-text available
Background Evaluation of X-ray reject analysis is an important quality parameter in diagnostic facility. The aim of this study was to find out the radiograph rejection and its causes during the coronavirus disease 2019 (COVID-19) pandemics as there was fear of coronavirus disease infection among the technical staff from the incoming patients in a busy, high volume public sector tertiary care hospital.Materials and methodThis descriptive study was conducted at Radiology Department, Lady Reading Hospital, Peshawar from August to November, 2020. The rejected radiographs and their causes were analyzed.ResultsA total of 15,000 X-ray procedures were conducted during study period out of which 2550 cases were repeated making the total rejection 17%. Rejection in male and female were 74.3 and 25.7%, respectively, while rejection in adults was (80.1%) and (19.9%) in pediatric age group of the total rejection. The main cause of rejection was positioning (30.5%) followed by artifacts (22.4%), motion (12.1%), improper collimation (10%), wrong labeling (8.4%), exposure errors (6.9%), detector errors (3.7%), machine faults (2.8%), re-request from referring physician (1.7%), and PACS issues (1.5%). In terms of body anatomical parts, the highest rejection was observed in extremities (44.1%), followed by chest radiography (23.3%), spine (11.4%), abdomen (6.4%), skull (5.9%), pelvis (4.7%), KUB (3.7%), and neck (0.6%), respectively.Conclusion Radiograph rejection is common problem in every diagnostic facility but significant reduction can be achieved by implementing rejection analysis as basic quality indicator, and conducting technologist/s specific training programs for their knowledge and skill enhancement.
Article
Background: Skull radiography, an assessment method for initial diagnosis and post-operative follow-up, requires substantial retaking of various types of radiographs. During retaking, a radiologic technologist estimates a patient's rotation angle from the radiograph by comprehending the relationship between the radiograph and the patient's angle for adequate assessment, which requires extensive experience. Objective: To develop and test a new deep learning model or method to automatically estimate patient's angle from radiographs. Methods: The patient's position is assessed using deep learning to estimate their angle from skull radiographs. Skull radiographs are simulated using two-dimensional projections from head computed tomography images and used as input data to estimate the patient's angle, using deep learning under supervised training. A residual neural network model is used where the rectified linear unit is changed to a parametric rectified linear unit, and dropout is added. The patient's angle is estimated in the lateral and superior-inferior directions. Results: Applying this new deep learning model, the estimation errors are 0.56±0.36° and 0.72±0.52° in the lateral and superior-inferior angles, respectively. Conclusions: These findings suggest that a patient's angle can be accurately estimated from a radiograph using a deep learning model leading to reduce retaking time, and then used to facilitate skull radiography.
Chapter
Diagnostics is a crucial tool in health care’s endeavours to help people, and tremendous progress has been made in the field. Nonetheless, there are a wide range of uncertainties involved in all aspects of diagnostics – uncertainties that are important for scientific improvement, for quality of care, for practicing medicine, for informing patients, and for health policy making. In this chapter we analyse a wide range of uncertainties presenting in the various steps of diagnostic imaging. For each step we describe the main concern and suggest measures to reduce and handle the various kinds of uncertainty. Overall, we provide 9 specific measures to reduce uncertainty in the diagnostic process. Moreover, we analyse ethical issues related to the various types of uncertainty presenting at each step and end the chapter with five specific questions framed to raise the awareness of uncertainty in diagnostic imaging, as well as to reduce and to handle it. Thereby we hope that this chapter will provide practical measures to acknowledge and address diagnostic uncertainty.
Article
Purpose This study was undertaken to investigate and compare the reject rate and causes of rejection between conventional film-screen radiography and computed radiography with PACS in the same radiology department. Methods Rejected radiographs of conventional film-screen radiography were collected over a 12-month period. After the installation of a computed radiography system and picture archiving and communication system (PACS), rejected images were also recorded at the quality assurance workstation for 12 months. The rejected radiographs and images were analyzed and categorized into seven groups according to the causes of rejection. Results The overall reject rate of computed radiography with PACS (1.3%) was significantly lower than that of conventional radiography (2.1%). In conventional radiography, exposure (38.6%) and positioning (28.2%) errors were the main reasons for rejection, whereas the main reason in computed radiography was positioning errors (55.4%). Rejection due to exposure errors and patient movement in computed radiography (7.4% and 2.3%, respectively) are significantly lower than those in conventional radiography (38.6% and 6.5%, respectively). Conclusions With the use of computed radiography and PACS, the overall reject rate is reduced when compared to conventional film-screen radiography. Unnecessary radiation exposure to patients due to image retake can be reduced. © 2004 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.
Article
Purpose: Achieving cost-effectiveness within the NHS is an old initiative but one that has again been highlighted by recent government policies (The New NHS—Modern and Dependable, Stationary Office, London, 1997). It has been reiterated that it is the responsibility of individual Trusts to devise means to provide such a service. Reject/repeat analyses have long been the primary tool used to assess the cost-effectiveness of radiography departments (Quality Assurance in Diagnostic Radiology, WHO, Geneva, 1982). This research paper examines an in-house initiative (viewing patients' previous films) commonly employed in other Health Trusts in order to reduce departmental repeat/reject rates.Method: Three hundred orthopaedic patients with hip, knee and ankle prostheses were included in a reject/repeat analysis. The aim was to investigate whether or not viewing patient's previous relevant radiographs would be advantageous to the practicing radiographer. This was done through an audit cycle consisting of two audit periods each lasting for 3 months. The primary audit period recorded the baseline repeat/reject rate, with the secondary audit period recording the repeat/reject rate under an experimental condition of viewing the relevant radiographs.Results: The baseline audit revealed repeat rates of 33% in orthopaedic patients with hip, knee and ankle prostheses. The availability of prior film viewing to the radiographer reduced this repeat rate to 10.6%.Conclusion: Prior film viewing dramatically reduced the department's repeat/reject rate by 22.4%. This provides scope for significant patient dose reductions as well as reducing departmental film expenses. This is an underestimated initiative and should be used appropriately in routine clinical practice.
Article
Achieving quality with cost effectiveness in the NHS is the goal of current government initiatives, but how is quality measured? Within the NHS, individual Trusts and primary care groups have a statutory responsibility for quality but clinicians remain individually responsible and accountable for their clinical practice and the quality of care that they give. To assure a quality service, service levels need to be measured within the clinical environment and a potential measurement tool is film reject analysis. This paper reviews some of the current quality drivers within the NHS and their impact on clinical practice and it aims to evaluate the role of reject analysis in the modern imaging department, identifying areas where reject analysis can assist in the improvement of service quality and cost-effectiveness. Particular attention is given to the role of reject analysis in directing and supporting the clinical development and role extension of the radiographer in line with government quality directives and clinical governance.
Article
In medical radiography, retakes, for repeat examinations are a source of unnecessary exposure to the patient. This investigation was undertaken to develop a methodology for the study of retakes necessitated by the production of unacceptable diagnostic radiographs. The methodology was to provide the means for: (1) ascertaining the extent of the retake problem, (2) determining the most common reasons for retake examinations and the causal agents and (3) indicating corrective measures needed to reduce the frequency of retakes. The result of this development, a series of forms and formats for data recording, represents a programme for evaluation of one aspect of the performance of a radiographic facility. Data based upon approximately 30000 films from a hospital-based pilot project were used to illustrate the application of this programme to: (1) the determination of the frequency and causes of retakes for the most commonly performed radiographic procedures and (2) the relation of the causes of retakes to aspects of equipment performance and technologist's experience. Guidelines for assessing the results of data analysis and implementing corrective measures were also presented.
Article
PurposeReject analysis has been used as a quality indicator of radiology services and has been recommended as a quality indicator for purchasers. The aim of the study was to measure the reject and repeat rate and also to evaluate its use as a quality indicator.MethodsThe reject rate was measured for each plain film examination using a form given to radiographers to record each rejected film. The results were then collected and entered into a database for analysis.ResultsThe reject rate has been measured for a large department across all plain film examinations. Analysis has shown the overall reject rate to be 8% in approximately 33 000 examinations. Individual examination results were chest=6.5%, abdomen = 4%, knees = 26%, lumbar spine = 14.3%, thoracic spine=18.6%, and cervical spine= 10%. Chest X-rays account for approximately 50% of all examinations and dominate the overall reject rate. Twenty-five percent of films reassessed by radiologists were considered to be of diagnostic quality.ConclusionReliance upon the overall reject rate may camouflage problems in individual examinations. As a result of this study care is recommended when using reject rates as a global quality indicator for radiology services, but its use is supported in local, examination specific, audits for quality.
Article
The objective of this work was to evaluate the influence of the postprocessing tool Diamond View (Siemens AG Medical Solutions, Germany) on image quality in conventional chest radiography. Evaluation of image quality remains a challenge in conventional radiography. Based on the European Commission quality criteria we evaluated the improvement of image quality when applying the new postprocessing tool Diamond View (Siemens AG Medical solutions, Germany) to conventional chest radiographs. Three different readers prospectively evaluated 102 digital image pairs of chest radiographs. Statistical analysis was performed with a p value <0.05 considered as significant. Images were evaluated on basis of the modified imaging Quality Criteria by the Commission of the European Communities. Each of the 11 image quality criteria was evaluated separately using a five point classification. Statistical analysis showed an overall tendency for improved image quality for Diamond View (DV) for all criteria. Significant differences could be found in most of the criteria. In conclusion DV improves image quality in conventional chest radiographs.
Article
Quality assurance is essential in the cost-effective provision of radiologic services. Technical aspects include the routine measurement of equipment performance and the control of reject and retake rates. The current emphasis is on reducing patient radiation dose while maintaining acceptable image quality. The reduction of unnecessary radiologic investigations is also a priority. Clinical and medical audits, integral parts of the quality assurance process, form part of a continuously repeated cycle designed to raise standards of performance by changing clinical practice on the basis of outcome analysis. Quality assurance in mammography is presented as an example.
Article
The radiographic film wastage and the different parameters affecting this wastage were analysed for a 9-week period at a 600-bed University Hospital. An overall reject rate of 7.6% was found. The different reasons for rejection were evaluated, while retake rate, relation between working experience of the personnel, amount of rejected films and total film wastage in surface (m2), were registered and analysed.
Article
Absolute certainty in diagnosis is unattainable, no matter how much information we gather, how many observations we make, or how many tests we perform. A diagnosis is a hypothesis about the nature of a patient's illness, one that is derived from observations by the use of inference.1 2 3 As the inferential process unfolds, our confidence as physicians in a given diagnosis is enhanced by the gathering of data that either favor it or argue against competing hypotheses. Our task is not to attain certainty, but rather to reduce the level of diagnostic uncertainty enough to make optimal therapeutic decisions.4 5 6 7 Testing is . . .