Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control

Stephen M. Kengyelics; Amber J. Gislason-Lee; Claire Keeble; Derek R. Magee; Andrew G. Davies

doi:10.1117/1.JEI.24.5.051002

21 September 2015 Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control

Stephen M. Kengyelics, Amber J. Gislason-Lee, Claire Keeble, Derek R. Magee, Andrew G. Davies

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 5, 051002 (September 2015). https://doi.org/10.1117/1.JEI.24.5.051002

Abstract

Modern cardiac x-ray imaging systems regulate their radiation output based on the thickness of the patient to maintain an acceptable signal at the input of the x-ray detector. This approach does not account for the context of the examination or the content of the image displayed. We have developed a machine vision algorithm that detects iodine-filled blood vessels and fits an idealized vessel model with the key parameters of contrast, diameter, and linear attenuation coefficient. The spatio-temporal distribution of the linear attenuation coefficient samples, when appropriately arranged, can be described by a simple linear relationship, despite the complexity of scene information. The algorithm was tested on static anthropomorphic chest phantom images under different radiographic factors and 60 dynamic clinical image sequences. It was found to be robust and sensitive to changes in vessel contrast resulting from variations in system parameters. The machine vision algorithm has the potential of extracting real-time context sensitive information that may be used for augmenting existing dose control strategies.

Citation Download Citation

Stephen M. Kengyelics, Amber J. Gislason-Lee, Claire Keeble, Derek R. Magee, and Andrew G. Davies "Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control," Journal of Electronic Imaging 24(5), 051002 (21 September 2015). https://doi.org/10.1117/1.JEI.24.5.051002

Published: 21 September 2015

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