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A method to optimize the processing algorithm of a computed radiography system for chest radiography

¹ Radiation Physics Department, Hull & East Yorkshire Hospitals, Princess Royal Hospital, Saltshouse Road, Kingston Upon Hull HU8 9HE, ² Centre for Magnetic Resonance Imaging, University of Hull, Hull Royal Infirmary, Kingston Upon Hull HU3 2JZ, ³ Postgraduate Medical Institute, University of Hull, Kingston Upon Hull HU6 7RX, ⁴ Faculty of Health and Wellbeing, Sheffield Hallam University, City Campus, Howard Street, Sheffield S1 1WB, UK

Correspondence: C S Moore Radiation Physics Department, Hull & East Yorkshire Hospitals, Princess Royal Hospital, Saltshouse Road, Kingston Upon Hull HU8 9HE, UK. E-mail: craig.moore{at}hey.nhs.uk

A test methodology using an anthropomorphic-equivalent chest phantom is described for the optimization of the Agfa computed radiography "MUSICA" processing algorithm for chest radiography. The contrast-to-noise ratio (CNR) in the lung, heart and diaphragm regions of the phantom, and the "system modulation transfer function" (sMTF) in the lung region, were measured using test tools embedded in the phantom. Using these parameters the MUSICA processing algorithm was optimized with respect to low-contrast detectability and spatial resolution. Two optimum "MUSICA parameter sets" were derived respectively for maximizing the CNR and sMTF in each region of the phantom. Further work is required to find the relative importance of low-contrast detectability and spatial resolution in chest images, from which the definitive optimum MUSICA parameter set can then be derived. Prior to this further work, a compromised optimum MUSICA parameter set was applied to a range of clinical images. A group of experienced image evaluators scored these images alongside images produced from the same radiographs using the MUSICA parameter set in clinical use at the time. The compromised optimum MUSICA parameter set was shown to produce measurably better images.

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