Quantitative assessment of computed radiography quality control parameters

Abstract

Quality controls for testing the performance of computed radiography (CR) systems have been recommended by manufacturers and medical physicists' organizations. The purpose of this work was to develop a set of image processing tools for quantitative assessment of computed radiography quality control parameters. Automatic image analysis consisted in detecting phantom details, defining regions of interest and acquiring measurements. The tested performance characteristics included dark noise, uniformity, exposure calibration, linearity, low-contrast and spatial resolution, spatial accuracy, laser beam function and erasure thoroughness. CR devices from two major manufacturers were evaluated. We investigated several approaches to quantify the detector response uniformity. We developed methods to characterize the spatial accuracy and resolution properties across the entire image area, based on the Fourier analysis of the image of a fine wire mesh. The implemented methods were sensitive to local blurring and allowed us to detect a local distortion of 4% or greater in any part of an imaging plate. The obtained results showed that the developed image processing tools allow us to implement a quality control program for CR with short processing time and with absence of subjectivity in the evaluation of the parameters.

Method for automatic detection of defective ultrasound linear array transducers based on uniformity assessment of clinical images — A case study

“…Assessment of the image uniformity as part of a quality assurance program is used for other imaging modalities as well, such as e.g., computed26, 27 and digital28 radiography systems and in nuclear medicine 29, 30. To use median images of clinical images for detecting nonuniformities in these systems may also be possible, and could be useful as a complement to quality assurance for early detection of a subset of defects.…”

Automated quality control in Computed Radiography

“…The application automatically opens image sets acquired sequentially, identifies the IP by reading its identification number extracted from the administrative section of the DICOM file, identifies the type of check based on the file name, calculates the parameters of interest, and creates for every IP a table listing the numerical results of the check. The magnitudes used for quality checks, the reference protocols [7,9,10] and the reference values adopted are listed in Table 2 and briefly described below.…”

“…Uniformity was quantitatively verified by assessing the [7,9,10] difference in response of various zones of the detector subjected to uniform exposure. The numerical parameters [10] obtained indicate the extent of deviation from the behaviour of an ideal detector, which, if exposed uniformly, should ideally yield a uniform and constant response.…”

“…The numerical parameters [10] obtained indicate the extent of deviation from the behaviour of an ideal detector, which, if exposed uniformly, should ideally yield a uniform and constant response. To verify the absence of artefacts, the software also includes an automatic detection algorithm that segments the image and highlights nonuniform regions above a set threshold, thus providing a better depiction of possible artefacts.…”

“…Noise and low-contrast resolution characteristics were posto ad un'esposizione uniforme. I parametri numerici [10] Dark noise Kodak systems: Kodak EI value <100 EI value <100 guidelines [7] Philips systems:…”