Yolanda Georgia Koutraki1,2, Rachael O. Forsythe2, Chengjia Wang1,3, Olivia Mcbride2, Jennifer Robson2, Tom J. MacGillivray1, Calum D. Gray1, David E. Newby1,2, and Scott I. Semple1,2
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
Synopsis
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.Introduction
Abdominal aortic aneurysms (AAA) are responsible for 1-3% of
deaths in men aged 65 to 85 in the western world1. Currently
decisions for AAA repairs are based on ultrasound measures of the aneurysm diameter
(>5.5cm), which is an imperfect criterion since 60% of AAA>5.5 cm never
rupture, while 10-20% of AAA< 5.5 cm do rupture
2,3. Ruptured AAA
cause 80%-90% mortality, so there is an imperative need for better methods to
accurately predict AAA expansion and rupture.
Richards et al
4 demonstrated that uptake of
Ultrasmall Superparamagnetic Particles of Iron Oxide (USPIO) in MRI identifies
cellular inflammation, while differentiation in patterns of inflammation
correlates with aneurysm growth-rate: AAA with distinct mural uptake of USPIO
(“inflammatory hotspots”) were found to expand significantly faster.
This processing of the data on a 2D slice-by-slice basis
however is time-consuming and it uses an empirically-defined threshold which may
exclude important information, while inter- and intra-observer variability are
introduced by subsequent manual classification.
We previously suggested the use of a classification
technique
6 which automatically detects hotspots of inflammation and
classifies AAA.
We have now developed our algorithm to include 3D processing
of the data. The inflammation throughout the whole volume of the AAA can be
quantified and visualised for the first time; this enables us to begin
sub-classification of the current groups and higher accuracy of growth
prediction in our existing classification.
We are also incorporating anatomical measurements to further
assist our classification with multivariate analysis.
Our algorithm is now included in a Graphical User Interface (GUI)
and we have enabled batch processing to greatly reduce classification time.
Materials and Methods
350
patients were imaged using a 3-T MRI Verio (Siemens GmbH, Erlangen) before and 24+
hours after administration of USPIO (Rienso); sub-groups were randomly selected
for our algorithm to be tested. A multi-echo, gradient-echo T2*W sequence was
used to produce T2* maps to detect the accumulation of USPIO within the AAA.
The percentage change in T2* (%ΔT2*) was calculated and displayed as a colour
scale. The datasets were registered automatically using a previously described
custom algorithm
5. Our program was built in MATLAB-R2015a (Mathworks)
and uses non-thresholded data. The periluminal area of the AAA is automatically
masked. In order to detect ‘hotspots’ of USPIO uptake, an adapted k-means
clustering (k=7) algorithm and 2D and 3D-connectivity are applied to the %ΔT2*
data. Metrics (eg. lumen size and shape) are calculated using MATLAB and the 3D
visualisations are created in MATLAB and Paraview (Kitware).
Results
In the subpopulation of 16 patients initially processed, classification
of 12 out of 16 patients was in agreement between the automatic classification
and the clinicians’ manual classification (92% of hotspots agreed). However
when we checked the outcome of the percentage US growth of the AAA at one year,
the automatic classification was more predictive of growth than manual
classification (Figure 1). This might be the result of using non-thresholded
data in the automatic processing, so that the automatically detected 2D
hotspots appear larger and therefore less potential hotspots are discarded
(Figures 2, 3). We are now in the process of using the 3D-connectivity between
hotspots of different slices and the metrics to subclassify the AAA according
to hotspot size and shape. The total processing time with our program for each
patient ranges between 70 to 95 seconds. The corresponding processing time by trained
observers ranges between 45 to 65 minutes per patient per observer.
Discussion
Our automatic classification program appears to have a high success
rate in reproducing the clinicians’ manual classification, while
introducing improvements to the process that increase aneurysm
growth-rate prediction accuracy. This software may provide clinicians
with more automated, robust and fast data processing and can effectively
assist in the assessment of future AAA patients. By using
non-thresholded data both in 2D and 3D, we obtain more reliable
measurements of USPIO uptake, including areas missed in manual
processing. The clustering technique used in our algorithm adapts to
every individual patient, while the 71% threshold used in the manual
processing is population-based. The processing time of the program is
approximately 40 times faster than the manual processing, without taking
into consideration the extra time needed for observer training. The
results are fully reproducible removing inter- and intra-observer
variability. With the incorporation of anatomical metrics and 3D
connectivity information we have the opportunity to investigate
further sub-classifications within the AAA patients. Furthermore, these
techniques can be adapted in the future to assist with the imaging of
inflammation throughout the body in different clinical application, for
example USPIO uptake targeting inflammation post myocardial infarction.
Acknowledgements
This work is funded by the Medical Research Council, British Heart Foundation and the Scottish Universities Physics Alliance INSPIRE award.References
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Sakalihasan, N., R. Limet, and O.D. Defawe, Lancet, 2005.
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Hardman, D., et al., International Journal for Numerical Methods in Biomedical
Engineering, 2013.
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et al., European Journal of Vascular and Endovascular Surgery, 2001.
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2011.
5. Wang, et al., ISMRM annual meeting, 2014.
6. Y.G.
Koutraki, et al., ISMRM annual meeting, 2015.