Abstract #2630

Automatic Classification and 3D Visualisation of Abdominal Aortic Aneurysms to Predict Aneurysm Expansion and Rupture

Yolanda Georgia Koutraki^1,2, Rachael O. Forsythe², Chengjia Wang^1,3, Olivia Mcbride², Jennifer Robson², Tom J. MacGillivray¹, Calum D. Gray¹, David E. Newby^1,2, and Scott I. Semple^1,2

¹Clinical Research Imaging Centre, University of Edinburgh, Edinburgh, United Kingdom, ²Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom, ³Toshiba 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. Uptake of Ultrasmall Superparamagnetic Particles of Iron Oxide (USPIO) in AAA has been shown to correlate with aneurysm growth-rate. We previously suggested the use of an automatic AAA classification technique in order to replace manual processing. We have now improved our algorithm to include 3D data analysis and visualisation, multivariate analysis of metrics, batch processing and a Graphical User Interface. We are improving growth prediction with full reproducibility, 40 times faster than before.

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