An image‐based technique to assess the perceptual quality of clinical chest radiographs

PURPOSE: To develop and validate a psychometric scale for assessing Image quality perception for chest radiographs.METHODS: A review of the literature was undertaken to identify items/factors which could be used to evaluate image quality using a perceptual approach. A draft scale was then created (22 items) and presented to a focus group (student, qualified radiographers and radiology registers). Within the focus group the draft scale was discussed and modified. A series of seven postero-anterior chest images were generated using a phantom with a range of image qualities. Image quality perception was initially confirmed for the seven images using signal-to-noise ratio (SNR) and group consensus. Participants were invited to independently score each of the images using the draft image quality perception scale. Bandura's theory was used to guide scale development and Cronbach alpha was used to test interval reliability.RESULTS: Fifty three participants used the scale to grade image quality perception on each of the seven images (SNR 17.2 to 36.5). Aggregated mean scale score increased with increasing SNR from 42.1 to 87.7 (r=0.98, P<0.001). For each of the 22 individual scale items there was clear differentiation of low, mid and high-quality images. A Cronbach alpha coefficient of >0.7 was obtained across each of the seven images.CONCLUSION: This study represents the first development of a chest image quality perception scale based on Bandura's theory. There was excellent correlation between the image quality perception scores derived using the scale and the SNR and group consensus. Further research will involve a more detailed item and factor analysis.

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“…SNR was calculated for the 35 images using four regions [24]. Seven images were selected, representing low to high SNR.…”

Section: Image Selectionmentioning

confidence: 99%

Increasing source to image distance for AP pelvis imaging – Impact on radiation dose and image quality

et al. 2014

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“…A more recently reported automated platform is limited by its reliance on phantom measurements and the complexity associated with image quality in nonuniform patient images [9,10]. Automated approaches to assess image quality in chest radiography have been noteworthy [11]. However, such methods have not been implemented for CT.…”

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confidence: 99%

Automated Technique to Measure Noise in Clinical CT Examinations

Christianson

Winslow

Frush

et al. 2015

American Journal of Roentgenology

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100

133

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The global noise level provides an accurate, robust, and automated method to measure CT noise in clinical examinations for quality assurance programs. The significant difference in noise across scanner models indicates the unexploited potential to efficiently assess and subsequently improve protocol consistency. Combined with other automated characterization of imaging performance (e.g., dose monitoring), the global noise level may offer a promising platform for the standardization and optimization of CT protocols.

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“…The images were anonymized, securely transferred, and analyzed by the Duke University Clinical Imaging Physics Group using a previously described24 and validated25 software program. The software automatically segments each image and measures 10 perceptual attributes of chest radiographs.…”

Section: Methodsmentioning

confidence: 99%

Medical physics 3.0 versus 1.0: A case study in digital radiography quality control

Carver

Willis

Stauduhar

et al. 2018

J Applied Clin Med Phys

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PurposeThe study illustrates how a renewed approach to medical physics, Medical Physics 3.0 (MP3.0), can identify performance decrement of digital radiography (DR) systems when conventional Medical Physics 1.0 (MP1.0) methods fail.Methods MP1.0 tests included traditional annual tests plus the manufacturer's automated Quality Assurance Procedures (QAP) of a DR system before and after a radiologist's image quality (IQ) complaint repeated after service intervention. Further analysis was conducted using nontraditional MP3.0 tests including longitudinal review of QAP results from a 15‐yr database, exposure‐dependent signal‐to‐noise (SNR 2), clinical IQ, and correlation with the institutional service database. Clinical images were analyzed in terms of IQ metrics by the Duke University Clinical Imaging Physics Group using previously validated software.ResultsTraditional metrics did not indicate discrepant system performance at any time. QAP reported a decrease in contrast‐to‐noise ratio (CNR) after detector replacement, but remained above the manufacturer's action limit. Clinical images showed increased lung noise (Ln), mediastinum noise (Mn), and subdiaphragm‐lung contrast (SLc), and decreased lung gray level (Lgl) following detector replacement. After detector recalibration, QAP CNR improved, but did not return to previous levels. Lgl and SLc no longer significantly differed from before detector recalibration; however, Ln and Mn remained significantly different. Exposure‐dependent SNR 2 documented the detector operating within acceptable limits 9 yr previously but subsequently becoming miscalibrated sometime before four prior annual tests. Service records revealed catastrophic failure of the computer containing the original detector calibration from 11 yr prior. It is likely that the incorrect calibration backup file was uploaded at that time.Conclusions MP1.0 tests failed to detect substandard system performance, but MP3.0 methods determined the root cause of the problem. MP3.0 exploits the wealth of data with more sensitive performance indicators. Data analytics are powerful tools whose proper application could facilitate early intervention in degraded system performance.

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An image‐based technique to assess the perceptual quality of clinical chest radiographs

Abstract: The measured physical quantities provide a robust reflection of perceptual image quality in clinical images. The methodology can be readily applied for automated evaluation of perceptual image quality in clinical chest radiographs.

Cited by 22 publications

References 27 publications

Increasing source to image distance for AP pelvis imaging – Impact on radiation dose and image quality

Increasing source to image distance for AP pelvis imaging – Impact on radiation dose and image quality

Automated Technique to Measure Noise in Clinical CT Examinations

Medical physics 3.0 versus 1.0: A case study in digital radiography quality control

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