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Image quality versus quality control/assurance

Having identified the important link between image quality and radiation dose in diagnostic radiography, it is now necessary that image quality is employed as a form of quality control, and also as a quality assurance measure of imaging system performance (Carmichael, 1989; BIR, 1989a). A variety of quality control frameworks that assess image quality have been described in the literature. For instance, in 2001, Kotre and Marshall considered the quality control protocol for image quality in digital fluorography, and Karoussou (2005) reviewed the measurement standards of image quality in digital X-ray systems. The parameters that have been used within both studies are noise, sharpness and the level of image distortion (mostly physical). These measures (i.e. quality control protocols) are intended to reduce the amount of error that can be associated with the imaging process and how it could potentionally affect the radiographic image quality (Zoetelief, Soldt, Suliman, Jansen, & Bosmans, 2005). As a result, an image with poor quality, caused by poor imaging system performance, could have an impact on the accuracy of the subsequent medical decisions and therefore influence patient health problem management.

The term ‘image’ refers to the visual communication of information produced by a given imaging system. By contrast, the word ‘quality’ can be defined as ‘a set of factors or perceptions by which a given image can be judged’. For instance, the quality in diagnostic radiography is closely related to the diagnostic capability of an image, in which there are specified protocols for making this assessment (Engeldrum, 1999). Aside from the physical and technical factors, it is important to take into account human perception and cognition when assessing radiogarpgic images. Image perception refers to the unified realisation of the content of the image (image signal), whereas human cognition can be defined as the ability to determine the meaning of the image in the context of the medical problem. Accordingly, this may affect the process of image quality evaluation (Krupinski, 2011; Kundel, 2006).

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