Patient-Specific Mechanical Characterization Of Abdominal Aortic Aneurysms Using 4D Ultrasound

Introduction

Abdominal aortic aneurysms (AAA) are silent killers and the 13th cause of death in Western society. In this study, methods for wall stress analysis (WSA) and elastography (EL) were developed using 4 D ultrasound (US) to determine patient-specific wall stresses and material properties. These techniques were introduced in the clinic and tested in a subgroup of patients in an ongoing study with 300 patients in follow-up.

Methods

In forty patients (AAA diameter 27 52mm), 4D-US data were measured using a Philips iU22 (X6 - 1 transducer). The brachial blood pressure was measured using an arm cuff. The US data were manually segmented. The patient-specific geometry was tracked over time to estimate its displacement field using 3D speckle tracking. Subsequently the diastolic geometry was converted into a finite element model. WSA was performed assuming a neo-Hookean material model. The model was optimized by iteratively adapting the material properties until the model output matched the 3 D displacements. For seven patients, computed tomography (CT) data were available and used to compare the US-based geometries and wall stresses.

Results

The 4D-US based 99th percentile wall stress ranged between 198 to 390 kPa, and the patient-specific material property (G(inc)) had a median of 1.1 MPa (IQR: 0.7 - 1.4 MPa). Geometry based on US data showed good similarity indices (0.90 - 0.96) with CT, and the 25th to 95th percentile wall stresses were in good agreement. Small aneurysms revealed stresses similar to those in large AAAs. Furthermore, the arterial stiffness increased with respect to AAA diameter.

Conclusion

This study shows that 4 D US-based WSA and EL of AAAs is feasible and has the potential to aid in AAA rupture risk assessment by identifying patients at risk, and to monitor patients over time by detecting changes in wall stress and material properties. Ongoing work includes a novel automatic segmentation and registration algorithm and long-term follow-up.