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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