Non-invasive Aortic Aneurysm Tissue Characterization Using Wall Viscoelasticity

BACKGROUND - Abdominal aortic aneurysm (AAA) rupture is the 13th cause of death in North America. The indications for a procedure to prevent rupture are based on AAA maximal diameter. However, it is a dichotomic criteria leading to under or over estimation of rupture risk. Biomechanics based criteria have been investigated using Finite Elements Analysis (FEA). The limitation of FEA is the absence of patient specific biomechanical information. Our team have previously developped a technique to map in-vivo the strain of AAAs, named non-invasive vascular elastography by ultrasound (NIVE). However, it relies on 2D ultrasound series, whereas new matrix- array probes allow a 3D mapping of the strain at a lower temporal resolution. We propose to create a 3D mapping of AAA strain with 3D ultrasound and register the strain mapping on a single phase CT.

In the project, we plan to acquire multi-modal imaging from 30 AAA patients scheduled for an elective repair will be recruited. A 3D NIVE examination and multiphase CT will be performed on each patient within less than one month of their surgery. A portion of the AAA wall will be harvested during surgery to correlate strains with tensile tests and histology. Following the implementation of NIVE processing and 3D registration, the NIVE and multiphase CT strain values will be calculated and if necessary controlled for the measurement of blood pressure, AAA maximal diameter, calcium load, gender and age. A FEA model developed at U of Calgary will be personalized using the calculation of in-vivo strains. Comparison of strain measurement between NIVE and multiphase CT, correlation of strain measurements and personalized FEA with AAA growth will constitute secondary endpoints. Following the complete validation of this personalized model, we anticipate this will provide predictive biomarkers for AAA vulnerability.

OBJECTIVES - Primary objectives: 1. Acquire a multimodal image dataset (multiphase CT, 3D US) from 30 AAA patients undergoing repair surgery (phase 1); 2. Optimize and validate in vitro the NIVE acquisition protocol and the image registration algorithm between NIVE and one phase of the multiphase CT (phase 1); 3. Compare the principal strain distribution assessed with 3D NIVE registered on conventional CT and those imaged with multiphase CT acquisition (phase 1); 4. Validate both approaches with histology and tensile test on AAA samples (phase 2).

Main hypothesis: Strain maps with 3D NIVE registered on single phase CT provide an accurate in-vivo estimation of AAA wall strength and yield similar accuracy to strain from multiphase CT to assess AAA vulnerability.

METHODOLOGY Phase 1 only will be done for the moment - Design: We plan a prospective study involving 30 AAA patients scheduled for elective open repair. For the phase 1 of the study, we will perform in all patients a NIVE examination and multiphase CT within less than one month of their surgery. For the phase 2 of the study, patient undergoing open surgery will be recruited. A portion of the AAA wall will be harvested during surgery (outside of scope of the pilot study) in order to perform biaxial tensile tests and histology examination. Patients will be recruited at the Centre hospitalier de l'Université de Montréal (CHUM) and Glen campus McGill University.

Data analysis: Spearman correlation, accuracy evaluated by mean error and standard deviation, intraclass correlation coefficient, Bland-Altmann analysis, mixed linear model to evaluate the influence of sex, MaxD, pulse pressure, calcification score on strain (NIVE, multiphase CT) measurements.

RATIONALE AND IMPACT - Ruptured AAA (rAAA) is a life-threatening situation which is the 13th mortality cause in USA. Endovascular aneurysm repair (EVAR) is often preferred for rAAA for its lower morbidity and mortality as compared with OR (1.6 versus 5%). But these data show the maximal diameter/rupture relationship to be nonlinear and inaccurate to predict rupture. New imaging biomarkers to predict AAA growth and rupture are therefore needed. There is also a need to personalize the tissue modeling with in vivo assessment of AAA wall strain to improve prediction of AAA rupture or growth. At the end of the study, we anticipate to have complete validation of this non-invasive assessment of AAA wall vulnerability using US-CT fusion,that will constitute the basis of a personalized assessment of AAA vulnerability. The strain mapping can be used to improve patient selection or be incorporated in a FEA model. Our personalized predictive model will also be useful for EVAR procedure simulation, planning and guidance.