A Geodesics-Based Surface Parameterization to Assess Aneurysm Progression

High-density thrombus and maximum transverse diameter on multi-spiral computed tomography angiography combine to predict abdominal aortic aneurysm rupture

Objective

We attempted to measure maximum transverse diameter (MTD) of and CT values of ILT by using multi-spiral computed tomography angiography (MSCTA) to investigate the predictive value of MTD with different CT values of thrombus on the risk of AAA rupture.

Methods

Forty-five intact abdominal aortic aneurysms (IAAA) and 17 ruptured abdominal aortic aneurysms (RAAA) were included in this study. MTD and CT values in their planes were measured from MSCTA images and aneurysm lumen and thrombus volumes were calculated for the range of different CT values.

Results

The median of maximum CT value of thrombus at the plane of MTD was higher in RAAA (107.0 HU) than the median in IAAA (84.5 HU) (P < 0.001). Univariate logistic regression analysis showed that the maximum CT value was a risk factor for RAAA (P < 0.001). It was further found that the area under the ROC curve for thrombus maximum CT value in the MTD plane to predict RAAA was 0.848 (P < 0.001), with a cut-off value of 97.5 HU, a sensitivity of 82.35%, and a specificity of 84.44%. And the MTD of the abnormal lumen combined with the maximum CT value at its plane predicted RAAA with an area under the ROC curve of 0.901, a sensitivity of 76.47%, and a specificity of 97.78%. The further analysis of thrombus volume in the range of different CT value showed that median thrombus volume in RAAA in the range of 30 HU~150 HU was 124.2 cm³ which was higher than the median of 81.4 cm³ in IAAA (P = 0.005). To exclude confounding factors (aneurysm volume), we calculated the standardized thrombus (ILT volume/total aneurysm volume), and the thrombus volume in the range of 30 HU~150 HU in RAAA was positively correlated with the standardized thrombus volume (ρ = 0.885, P < 0.001), while the thrombus volume in the range of −100 HU~30 HU was not correlated with it (ρ = 0.309, P = 0.228).

Conclusions

High-density ILT shown on MSCTA in AAAs is associated with aneurysm rupture, and its maximum transverse diameter combined with the maximum CT value in its plane is a better predictor of RAAA.

Visual Analysis of Aneurysm Data using Statistical Graphics

This paper presents a framework to explore multi-field data of aneurysms occurring at intracranial and cardiac arteries by using statistical graphics. The rupture of an aneurysm is often a fatal scenario, whereas during treatment serious complications for the patient can occur. Whether an aneurysm ruptures or whether a treatment is successful depends on the interaction of different morphological such as wall deformation and thickness, and hemodynamic attributes like wall shear stress and pressure. Therefore, medical researchers are very interested in better understanding these relationships. However, the required analysis is a time-consuming process, where suspicious wall regions are difficult to detect due to the time-dependent behavior of the data. Our proposed visualization framework enables medical researchers to efficiently assess aneurysm risk and treatment options. This comprises a powerful set of views including 2D and 3D depictions of the aneurysm morphology as well as statistical plots of different scalar fields. Brushing and linking aids the user to identify interesting wall regions and to understand the influence of different attributes on the aneurysm's state. Moreover, a visual comparison of pre- and post-treatment as well as different treatment options is provided. Our analysis techniques are designed in collaboration with domain experts, e.g., physicians, and we provide details about the evaluation.

Prediction of Abdominal Aortic Aneurysm Growth Using Dynamical Gaussian Process Implicit Surface

OBJECTIVE

We propose a novel approach to predict the Abdominal Aortic Aneurysm (AAA) growth in future time, using longitudinal computer tomography (CT) scans of AAAs that are captured at different times in a patient-specific way.

METHODS

We adopt a formulation that considers a surface of the AAA as a manifold embedded in a scalar field over the three dimensional (3D) space. For this formulation, we develop our Dynamical Gaussian Process Implicit Surface (DGPIS) model based on observed surfaces of 3D AAAs as visible variables while the scalar fields are hidden. In particular, we use Gaussian process regression to construct the field as an observation model from CT training image data. We then learn a dynamic model to represent the evolution of the field. Finally, we derive the predicted AAA surface from the predicted field along with uncertainty quantified in future time.

RESULTS

A dataset of 7 subjects (4-7 scans) was collected and used to evaluate the proposed method by comparing its prediction Hausdorff distance errors against those of simple extrapolation. In addition, we evaluate the prediction results with respect to a conventional shape analysis technique such as Principal Component Analysis (PCA). All comparative results show the superior prediction performance of the proposed approach.

CONCLUSION

We introduce a novel approach to predict the AAA growth and its predicted uncertainty in future time, using longitudinal CT scans in a patient-specific fashion.

SIGNIFICANCE

The capability to predict the AAA shape and its confidence region by our approach establish the potential for guiding clinicians with informed decision in conducting medical treatment and monitoring of AAAs.