Non-invasive Prediction of Peak Systolic Pressure Drop across Coarctation of Aorta using Computational Fluid Dynamics

Non-invasive Estimation of Pressure Drop Across Aortic Coarctations: Validation of 0D and 3D Computational Models with In Vivo Measurements

Blood pressure gradient ( Δ P ) across an aortic coarctation (CoA) is an important measurement to diagnose CoA severity and gauge treatment efficacy. Invasive cardiac catheterization is currently the gold-standard method for measuring blood pressure. The objective of this study was to evaluate the accuracy of Δ P estimates derived non-invasively using patient-specific 0D and 3D deformable wall simulations. Medical imaging and routine clinical measurements were used to create patient-specific models of patients with CoA (N = 17). 0D simulations were performed first and used to tune boundary conditions and initialize 3D simulations. Δ P across the CoA estimated using both 0D and 3D simulations were compared to invasive catheter-based pressure measurements for validation. The 0D simulations were extremely efficient ( ∼ 15 s computation time) compared to 3D simulations ( ∼ 30 h computation time on a cluster). However, the 0D Δ P estimates, unsurprisingly, had larger mean errors when compared to catheterization than 3D estimates (12.1 ± 9.9 mmHg vs 5.3 ± 5.4 mmHg). In particular, the 0D model performance degraded in cases where the CoA was adjacent to a bifurcation. The 0D model classified patients with severe CoA requiring intervention (defined as Δ P ≥ 20 mmHg) with 76% accuracy and 3D simulations improved this to 88%. Overall, a combined approach, using 0D models to efficiently tune and launch 3D models, offers the best combination of speed and accuracy for non-invasive classification of CoA severity.

Virtual Planning and Patient-Specific Graft Design for Aortic Repairs

PURPOSE

Patients presenting with coarctation of the aorta (CoA) may also suffer from co-existing transverse arch hypoplasia (TAH). Depending on the risks associated with the surgery and the severity of TAH, clinicians may decide to repair only CoA, and monitor the TAH to see if it improves as the patient grows. While acutely successful, eventually hemodynamics may become suboptimal if TAH is left untreated. The objective of this work aims to develop a patient-specific surgical planning framework for predicting and assessing postoperative outcomes of simple CoA repair and comprehensive repair of CoA and TAH.

METHODS

The surgical planning framework consisted of virtual clamp placement, stenosis resection, and design and optimization of patient-specific aortic grafts that involved geometrical modeling of the graft and computational fluid dynamics (CFD) simulation for evaluating various surgical plans. Time-dependent CFD simulations were performed using Windkessel boundary conditions at the outlets that were obtained from patient-specific non-invasive pressure and flow data to predict hemodynamics before and after the virtual repairs. We applied the proposed framework to investigate optimal repairs for six patients (n = 6) diagnosed with both CoA and TAH. Design optimization was performed by creating a combination of a tubular graft and a waterslide patch to reconstruct the aortic arch. The surfaces of the designed graft were parameterized to optimize the shape.

RESULTS

Peak systolic pressure drop (PSPD) and time-averaged wall shear stress (TAWSS) were used as performance metrics to evaluate surgical outcomes of various graft designs and implantation. The average PSPD improvements were 28% and 44% after the isolated CoA repair and comprehensive repair, respectively. Maximum values of TAWSS were decreased by 60% after CoA repair and further improved by 22% after the comprehensive repair. The oscillatory shear index was calculated and the values were confirmed to be in the normal range after the repairs.

CONCLUSION

The results showed that the comprehensive repair outperforms the simple CoA repair and may be more advantageous in the long term in some patients. We demonstrated that the surgical planning and patient-specific flow simulations could potentially affect the selection and outcomes of aorta repairs.

Blood-wall fluttering instability as a physiomarker of the progression of thoracic aortic aneurysms

The diagnosis of aneurysms is informed by empirically tracking their size and growth rate. Here, by analysing the growth of aortic aneurysms from first principles via linear stability analysis of flow through an elastic blood vessel, we show that abnormal aortic dilatation is associated with a transition from stable flow to unstable aortic fluttering. This transition to instability can be described by the critical threshold for a dimensionless number that depends on blood pressure, the size of the aorta, and the shear stress and stiffness of the aortic wall. By analysing data from four-dimensional flow magnetic resonance imaging for 117 patients who had undergone cardiothoracic imaging and for 100 healthy volunteers, we show that the dimensionless number is a physiomarker for the growth of thoracic ascending aortic aneurysms and that it can be used to accurately discriminate abnormal versus natural growth. Further characterization of the transition to blood-wall fluttering instability may aid the understanding of the mechanisms underlying aneurysm progression in patients.

Non-invasive estimation of pressure drop across aortic coarctations: validation of 0D and 3D computational models with in vivo measurements

Purpose

Blood pressure gradient (Δ P ) across an aortic coarctation (CoA) is an important measurement to diagnose CoA severity and gauge treatment efficacy. Invasive cardiac catheterization is currently the gold-standard method for measuring blood pressure. The objective of this study was to evaluate the accuracy of Δ P estimates derived non-invasively using patient-specific 0 D and 3 D deformable wall simulations.

Methods

Medical imaging and routine clinical measurements were used to create patient-specific models of patients with CoA (N = 17 ). 0 D simulations were performed first and used to tune boundary conditions and initialize 3 D simulations. Δ P across the CoA estimated using both 0 D and 3 D simulations were compared to invasive catheter-based pressure measurements for validation.

Results

The 0 D simulations were extremely efficient (~15 secs computation time) compared to 3 D simulations (~30 hrs computation time on a cluster). However, the 0 D Δ P estimates, unsurprisingly, had larger mean errors when compared to catheterization than 3 D estimates (12.1 ± 9.9 mmHg vs 5.3 ± 5.4 mmHg). In particular, the 0 D model performance degraded in cases where the CoA was adjacent to a bifurcation. The 0 D model classified patients with severe CoA requiring intervention (defined as Δ P ≥ 20 mmHg) with 76% accuracy and 3 D simulations improved this to 88%.

Conclusion

Overall, a combined approach, using 0 D models to efficiently tune and launch 3 D models, offers the best combination of speed and accuracy for non-invasive classification of CoA severity.

Effects of Uncertainty of Outlet Boundary Conditions in a Patient-Specific Case of Aortic Coarctation

Computational Fluid Dynamics (CFD) simulations of blood flow are widely used to compute a variety of hemodynamic indicators such as velocity, time-varying wall shear stress, pressure drop, and energy losses. One of the major advances of this approach is that it is non-invasive. The accuracy of the cardiovascular simulations depends directly on the level of certainty on input parameters due to the modelling assumptions or computational settings. Physiologically suitable boundary conditions at the inlet and outlet of the computational domain are needed to perform a patient-specific CFD analysis. These conditions are often affected by uncertainties, whose impact can be quantified through a stochastic approach. A methodology based on a full propagation of the uncertainty from clinical data to model results is proposed here. It was possible to estimate the confidence associated with model predictions, differently than by deterministic simulations. We evaluated the effect of using three-element Windkessel models as the outflow boundary conditions of a patient-specific aortic coarctation model. A parameter was introduced to calibrate the resistances of the Windkessel model at the outlets. The generalized Polynomial Chaos method was adopted to perform the stochastic analysis, starting from a few deterministic simulations. Our results show that the uncertainty of the input parameter gave a remarkable variability on the volume flow rate waveform at the systolic peak simulating the conditions before the treatment. The same uncertain parameter had a slighter effect on other quantities of interest, such as the pressure gradient. Furthermore, the results highlight that the fine-tuning of Windkessel resistances is not necessary to simulate the post-stenting scenario.