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Proceedings Paper

How much image noise can be added in cardiac x-ray imaging without loss in perceived image quality?
Author(s): Amber J. Gislason-Lee; Asli Kumcu; Stephen M. Kengyelics; Laura A. Rhodes; Andrew G. Davies
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Paper Abstract

Dynamic X-ray imaging systems are used for interventional cardiac procedures to treat coronary heart disease. X-ray settings are controlled automatically by specially-designed X-ray dose control mechanisms whose role is to ensure an adequate level of image quality is maintained with an acceptable radiation dose to the patient. Current commonplace dose control designs quantify image quality by performing a simple technical measurement directly from the image. However, the utility of cardiac X-ray images is in their interpretation by a cardiologist during an interventional procedure, rather than in a technical measurement. With the long term goal of devising a clinically-relevant image quality metric for an intelligent dose control system, we aim to investigate the relationship of image noise with clinical professionals’ perception of dynamic image sequences.

Computer-generated noise was added, in incremental amounts, to angiograms of five different patients selected to represent the range of adult cardiac patient sizes. A two alternative forced choice staircase experiment was used to determine the amount of noise which can be added to a patient image sequences without changing image quality as perceived by clinical professionals. Twenty-five viewing sessions (five for each patient) were completed by thirteen observers. Results demonstrated scope to increase the noise of cardiac X-ray images by up to 21% ± 8% before it is noticeable by clinical professionals. This indicates a potential for 21% radiation dose reduction since X-ray image noise and radiation dose are directly related; this would be beneficial to both patients and personnel.

Paper Details

Date Published: 16 March 2015
PDF: 6 pages
Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 93990L (16 March 2015); doi: 10.1117/12.2082788
Show Author Affiliations
Amber J. Gislason-Lee, Univ. of Leeds (United Kingdom)
Asli Kumcu, Univ. Gent (Belgium)
Stephen M. Kengyelics, Univ. of Leeds (United Kingdom)
Laura A. Rhodes, Univ. of Leeds (United Kingdom)
Andrew G. Davies, Univ. of Leeds (United Kingdom)


Published in SPIE Proceedings Vol. 9399:
Image Processing: Algorithms and Systems XIII
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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