RESUMO
PURPOSE: To implement in software the procedures described in AAPM Task Group 150's draft recommendations for image receptor performance testing, and to evaluate the effectiveness and practicality of these procedures. METHODS: Images of flat fields were acquired using digital x-ray image receptors at 6 cooperating institutions. Four flat field images obtained with each detector spanned a range of input detector air kerma. Software based on AAPM TG150's draft report processed the test images and generated results. Image receptor response and several measures of non-uniformity were evaluated. Images were divided into 10 mm square regions, after eliminating 10 mm borders. For each region, signal (mean), noise (standard deviation) and SNR were calculated. Characteristic signal, noise and SNR were calculated based on average values from all regions. Local non-uniformity for signal (SLN), noise (NLN) and SNR (SNRLN) were expressed as the maximum ratio of the absolute difference between each region's value and its 4 nearest neighbors, to the respective characteristic value. Global non-uniformity (SGN, NGN, SNRGN) were expressed similarly but differences between maximum and minimum values obtained from the regions were used (without comparison to local neighbors). RESULTS: TG150 tests discriminated between good and poorly performing detectors. Improper detector calibration was detectable, with noise non-uniformity proving to be a more sensitive measure than signal or SNR non-uniformity. Detector rotation relative to calibration conditions produced a greater change in signal non-uniformity than the other measures. Image receptor structured noise was characterized by an increase in noise non-uniformity with incident air kerma. CONCLUSIONS: AAPM TG150's proposed approach to image receptor testing was implemented and evaluated. The approach appears to be an effective and practical one for routine quality assurance testing of digital radiographic image receptors.
RESUMO
PURPOSE: Anomalous pixels may be defined as those pixels whose exposure response relationship is deviant from the typical, expected or calibrated response. A group of anomalous pixels may Result in visible correlated artifacts. Here we demonstrate an approach to identify anomalous pixels and correlated artifacts using flat-field images. METHODS: Using manufacturer specific calibration geometry, sets of four flat-field images per detector were obtained with varying input air kerma values (0.5 to 160 µGy) from 9 digital detectors at 6 institutions. Images obtained before and after calibration, with both proper and improper gain maps and structured artifacts were additionally acquired with some detectors. Image analysis methodology under consideration by AAPM Task Group 150 was used.After eliminating 10mm borders, images were divided into square regions (100mm2 ). Anomalous pixels were identified as pixels within each region with valuesabove or below ±3 standard deviations (SD) relative to the mean value of the region. If these pixels were identified in all four images comprising a set, then they were reported as anomalous. Line artifacts were identified as rows and columns with cumulative profile values that were above or below ±3 SD with respect to the mean value of neighboring profiles in the set of four flat-field mages. Results were verified with visual inspection of the images. RESULTS: For four sets of images, the algorithm did not identify any anomalous pixels, and none were spotted on visible inspection as well, while for five sets of images the identified anomalous pixels matched visual inspection results. Anomalous pixel detection failed in regions with an unusually large number of defects and structured noise, since those regions exhibited relatively large SD. Line artifacts consistent with visual analysis were identified correctly when present. CONCLUSIONS: A practical approach to identify anomalous pixels and correlated artifacts from flat-field images is demonstrated.