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Abstract #0175

Automatic detection of inflammatory hotspots in abdominal aortic aneurysms to identify patients at risk of aneurysm expansion and rupture

Yolanda Georgia Koutraki 1,2 , Chengjia Wang 1,3 , Jennifer Robson 2 , Olivia Mcbride 2 , Rachael O. Forsythe 2 , Tom J. MacGillivray 1 , Calum D. Gray 1 , Keith Goatman 3 , J. Camilleri-Brennan 2 , David E. Newby 1,2 , and Scott I. Semple 1,2

1 Clinical Research Imaging Centre, University of Edinburgh, Edinburgh, United Kingdom, 2 Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom, 3 Toshiba Medical Visualization System - Europe, Edinburgh, United Kingdom

The measurement of the diameter of abdominal aortic aneurysms (AAA) as a criterion for repair has been proved to be imperfect, thus new methods are required. Recently Ultrasmall Superparamagnetic Particles of Iron Oxide (USPIO) in AAA were shown to identify cellular inflammation in MRI scans and patients were classified in 3 groups based on the inflammation patterns. Group 3, with inflammatory hotspots on the aortic wall, was found to have a 3fold expansion of AAA. The classification process was manual and thus time-consuming and prone to inter- and intra-observer variability. We are suggesting the use of our automated classification software which has excellent agreement rates in hotspot detection, while it provides a 40 times faster, robust and objective processing, with the potential of sub-classification of the crucial patient group.

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