Yes / 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. / Project PANORAMA, funded by grants from Belgium, Italy, France, the Netherlands, United Kingdom, and the ENIAC Joint Undertaking.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/16975 |
| Date | 21 September 2015 |
| Creators | Kengyelics, S.M., Gislason-Lee, Amber J., Keeble, C., Magee, D.R., Davies, A.G. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
| Rights | Copyright 2015 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. |
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