Automatic classification and 3D visualisation of abdominal aortic aneurysms to predict aneurysm expansion

Abdominal aortic aneurysms (AAA) are a major cause of death in men above the age of 65 in the western world. Currently decisions for AAA management are based on the size of maximum AAA diameter (>5.5cm), measured using ultrasound imaging. However, as a proportion of AAAs rupture whilst still below this diameter threshold, while larger AAAs may never rupture, better methods for AAA expansion and rupture prediction are required. Previous research suggested that the presence of "hotspots" (focal areas) of inflammation as detected with USPIO-enhanced MRI may have potential in identifying faster-growing AAAs. However, the identification of these USPIO "hotspots" had been up to this point restricted to manual processing of the MRI data in a time-consuming and laborious slice-by-slice method, which only used 2D information. Inter- and intra- observer variability were an issue, as well as the use of empirically-defined signal thresholds which were dependent on each acquisition protocol. The work presented in this thesis aimed to evaluate current methodologies for AAA assessment and growth prediction and to contribute to improved prediction models by introducing novel techniques. Ultrasound was found to under-measure AAA size and the use of maximum AAA diameter was found to be problematic, especially for growth calculations. Automatically calculated alternatives which account for the total size and shape of the AAA, as measured with MRI, were introduced for more reproducible measurements. Furthermore, automation and standardisation of the previously-employed manual methods for hotspot detection and AAA classification were achieved, with the development of an efficient algorithm with excellent agreement levels. Taken a step further, two improved algorithms were introduced, adaptive to the data and USPIO distribution of individual AAAs and eliminating the universal threshold previously used. These algorithms incorporated information on 3D USPIO distribution along the length of the AAAs to detect and visualise 3D hotspots of inflammation for the first time. Novel 2D and 3D metrics were introduced, while the algorithms were also incorporated into a GUI for ease of clinical use. Additional aneurysm metrics automatically derived by the algorithms were incorporated into multiple linear regression models to investigate prediction of AAA growth rate. This investigation introduced three significant predictors which have not been used in previous predictive models of AAA expansion: the "mean thrombus major axis" metric, which reflected baseline size of AAA throughout multiple axial slices of the AAA; the "eccentricity WT" metric which reflected the relationship between wall shape and thrombus; and the presence of "3D hotspots" which may potentially reflect transported USPIO within a network of vascular channels along the length of the aneurysm. In line with previous literature, family history of AAA and high diastolic BP were also found to be significant predictors, but larger cohorts are needed for more reliable assessment of the predictive models suggested in this thesis.