Surgical Aortic Valve Replacement: Are We Able to Improve Hemodynamic Outcome?

Aortic valve replacement (AVR) does not usually restore physiological flow profiles. Complex flow profiles are associated with aorta dilatation, ventricle remodeling, aneurysms, and development of atherosclerosis. All these affect long-term morbidity and often require reoperations. In this pilot study, we aim to investigate an ability to optimize the real surgical AVR procedure toward flow profile associated with healthy persons. Four cases of surgical AVR (two with biological and two with mechanical valve prosthesis) with available post-treatment cardiac magnetic resonance imaging (MRI), including four-dimensional flow MRI and showing abnormal complex post-treatment hemodynamics, were investigated. All cases feature complex hemodynamic outcomes associated with valve-jet eccentricity and strong secondary flow characterized by helical flow and recirculation regions. A commercial computational fluid dynamics solver was used to simulate peak systolic hemodynamics of the real post-treatment outcome using patient-specific MRI measured boundary conditions. Then, an attempt to optimize hemodynamic outcome by modifying valve size and orientation as well as ascending aorta size reduction was made. Pressure drop, wall shear stress, secondary flow degree, helicity, maximal velocity, and turbulent kinetic energy were evaluated to characterize the AVR hemodynamic outcome. The proposed optimization strategy was successful in three of four cases investigated. Although no single parameter was identified as the sole predictor for a successful flow optimization, downsizing of the ascending aorta in combination with the valve orientation was the most effective optimization approach. Simulations promise to become an effective tool to predict hemodynamic outcome. The translation of these tools requires, however, studies with a larger cohort of patients followed by a prospective clinical validation study.

Patient-specific requirements and clinical validation of MRI-based pressure mapping: A two-center study in patients with aortic coarctation.

BACKGROUND: Invasive peak-to-peak pressure gradients are the current clinical reference standard for assessing aortic coarctation. To obtain them, patients need to undergo arterial heart catheterization. Unless an intervention is performed, the procedure remains purely diagnostic, while the concomitant risks remain. PURPOSE: To validate MRI-based pressure mapping against pressure drop derived from heart catheterization and to define minimal clinical requirements. STUDY TYPE: Prospective clinical validation study. POPULATION: Twenty-seven coarctation patients with an indicated heart catheterization were enrolled at two clinical centers. MRI SEQUENCES: 1.5T including 4D velocity-encoded MRI and 3D anatomical imaging of the aorta. ASSESSMENT: Pressure drop across the stenosis was calculated by pressure mapping based on the pressure Poisson equation. Calculated pressure drops were compared with catheter measured data. Spatial and temporal resolution were analyzed using in silico phantom-based data as well as in vivo measurements. STATISTICS: Pressure drop was compared to peak-to-peak measurements. A two-sample paired mean equivalence test was used. RESULTS: In patients without imaging artifacts and a required spatial resolution ≥5 voxel/diameter, significant equivalence of pressure mapping compared to heart catheterization was found (17.5 ± 6.49 vs. 16.6 ± 6.53 mmHg, P < 0.001). DATA CONCLUSION: Pressure mapping provides equivalent accuracy to pressure drop obtained from heart catheterization in patients 1) without previous stenting and 2) with sufficient spatial image resolution (at least 5 voxels/diameter). In these patients the method can reliably be performed prior to the actual procedure, and thus allows safe noninvasive treatment planning based on MRI. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:81-89.

Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH): uncertainty quantification of geometric rupture risk parameters.

BACKGROUND: Geometric parameters have been proposed for prediction of cerebral aneurysm rupture risk. Predicting the rupture risk for incidentally detected unruptured aneurysms could help clinicians in their treatment decision. However, assessment of geometric parameters depends on several factors, including the spatial resolution of the imaging modality used and the chosen reconstruction procedure. The aim of this study was to investigate the uncertainty of a variety of previously proposed geometric parameters for rupture risk assessment, caused by variability of reconstruction procedures. MATERIALS: 26 research groups provided segmentations and surface reconstructions of five cerebral aneurysms as part of the Multiple Aneurysms AnaTomy CHallenge (MATCH) 2018. 40 dimensional and non-dimensional geometric parameters, describing aneurysm size, neck size, and irregularity of aneurysm shape, were computed. The medians as well as the absolute and relative uncertainties of the parameters were calculated. Additionally, linear regression analysis was performed on the absolute uncertainties and the median parameter values. RESULTS: A large variability of relative uncertainties in the range between 3.9 and 179.8% was found. Linear regression analysis indicates that some parameters capture similar geometric aspects. The lowest uncertainties < 6% were found for the non-dimensional parameters isoperimetric ratio, convexity ratio, and ellipticity index. Uncertainty of 2D and 3D size parameters was significantly higher than uncertainty of 1D parameters. The most extreme uncertainties > 80% were found for some curvature parameters. CONCLUSIONS: Uncertainty analysis is essential on the road to clinical translation and use of rupture risk prediction models. Uncertainty quantification of geometric rupture risk parameters provided by this study may help support development of future rupture risk prediction models.

Hemodynamic Evaluation of a Biological and Mechanical Aortic Valve Prosthesis Using Patient-Specific MRI-Based CFD.

Modeling different treatment options before a procedure is performed is a promising approach for surgical decision making and patient care in heart valve disease. This study investigated the hemodynamic impact of different prostheses through patient-specific MRI-based CFD simulations. Ten time-resolved MRI data sets with and without velocity encoding were obtained to reconstruct the aorta and set hemodynamic boundary conditions for simulations. Aortic hemodynamics after virtual valve replacement with a biological and mechanical valve prosthesis were investigated. Wall shear stress (WSS), secondary flow degree (SFD), transvalvular pressure drop (TPD), turbulent kinetic energy (TKE), and normalized flow displacement (NFD) were evaluated to characterize valve-induced hemodynamics. The biological prostheses induced significantly higher WSS (medians: 9.3 vs. 8.6 Pa, P = 0.027) and SFD (means: 0.78 vs. 0.49, P = 0.002) in the ascending aorta, TPD (medians: 11.4 vs. 2.7 mm Hg, P = 0.002), TKE (means: 400 vs. 283 cm2 /s2 , P = 0.037), and NFD (means: 0.0994 vs. 0.0607, P = 0.020) than the mechanical prostheses. The differences between the prosthesis types showed great inter-patient variability, however. Given this variability, a patient-specific evaluation is warranted. In conclusion, MRI-based CFD offers an opportunity to assess the interactions between prosthesis and patient-specific boundary conditions, which may help in optimizing surgical decision making and providing additional guidance to clinicians.

Geometric uncertainty in intracranial aneurysm rupture status discrimination: a two-site retrospective study.

OBJECTIVES: Assessing the risk associated with unruptured intracranial aneurysms (IAs) is essential in clinical decision making. Several geometric risk parameters have been proposed for this purpose. However, performance of these parameters has been inconsistent. This study evaluates the performance and robustness of geometric risk parameters on two datasets and compare it to the uncertainty inherent in assessing these parameters and quantifies interparameter correlations. METHODS: Two datasets containing 244 ruptured and unruptured IA geometries from 178 patients were retrospectively analysed. IAs were stratified by anatomical region, based on the PHASES score locations. 37 geometric risk parameters representing four groups (size, neck, non-dimensional, and curvature parameters) were assessed. Analysis included standardised absolute group differences (SADs) between ruptured and unruptured IAs, ratios of SAD to median relative uncertainty (MRU) associated with the parameters, and interparameter correlation. RESULTS: The ratio of SAD to MRU was lower for higher dimensional size parameters (ie, areas and volumes) than for one-dimensional size parameters. Non-dimensional size parameters performed comparatively well with regard to SAD and MRU. SAD was higher in the posterior anatomical region. Correlation of parameters was strongest within parameter (sub)groups and between size and curvature parameters, while anatomical region did not strongly affect correlation patterns. CONCLUSION: Non-dimensional parameters and few parameters from other groups were comparatively robust, suggesting that they might generalise better to other datasets. The data on discriminative performance and interparameter correlations presented in this study may aid in developing and choosing robust geometric parameters for use in rupture risk models.

Hemodynamic Modeling of Biological Aortic Valve Replacement Using Preoperative Data Only.

Objectives: Prediction of aortic hemodynamics after aortic valve replacement (AVR) could help optimize treatment planning and improve outcomes. This study aims to demonstrate an approach to predict postoperative maximum velocity, maximum pressure gradient, secondary flow degree (SFD), and normalized flow displacement (NFD) in patients receiving biological AVR. Methods: Virtual AVR was performed for 10 patients, who received actual AVR with a biological prosthesis. The virtual AVRs used only preoperative anatomical and 4D flow MRI data. Subsequently, computational fluid dynamics (CFD) simulations were performed and the abovementioned hemodynamic parameters compared between postoperative 4D flow MRI data and CFD results. Results: For maximum velocities and pressure gradients, postoperative 4D flow MRI data and CFD results were strongly correlated (R 2 = 0.75 and R 2 = 0.81) with low root mean square error (0.21 m/s and 3.8 mmHg). SFD and NFD were moderately and weakly correlated at R 2 = 0.44 and R 2 = 0.20, respectively. Flow visualization through streamlines indicates good qualitative agreement between 4D flow MRI data and CFD results in most cases. Conclusion: The approach presented here seems suitable to estimate postoperative maximum velocity and pressure gradient in patients receiving biological AVR, using only preoperative MRI data. The workflow can be performed in a reasonable time frame and offers a method to estimate postoperative valve prosthesis performance and to identify patients at risk of patient-prosthesis mismatch preoperatively. Novel parameters, such as SFD and NFD, appear to be more sensitive, and estimation seems harder. Further workflow optimization and validation of results seems warranted.

Abnormal aortic flow profiles persist after aortic valve replacement in the majority of patients with aortic valve disease: how model-based personalized therapy planning could improve results. A pilot study approach.

OBJECTIVES: Complex blood flow profiles in the aorta are known to contribute to vessel dilatation. We studied flow profiles in the aorta in patients with aortic valve disease before and after surgical aortic valve replacement (AVR). METHODS: Thirty-four patients with aortic valve disease underwent 4-dimensional velocity-encoded magnetic resonance imaging before and after AVR (biological valve = 27, mechanical valve = 7). Seven healthy volunteers served as controls. Eccentricity (ES) and complex flow scores (CFS) were determined from the degree of helicity, vorticity and eccentricity of flow profiles in the aorta. Model-based therapy planning was used in 4 cases to improve in silico postoperative flow profiles by personalized adjustment of size, rotation and angulation of the valve as well as aorta diameter. RESULTS: Patients with aortic valve disease showed more complex flow than controls [median ES 2.5 (interquartile range (IQR) 2.3-2.7) vs 1.0 (IQR 1.0-1.0), P < 0.001, median CFS 4.7 (IQR 4.3-4.8) vs 1.0 (IQR 1.0-2.0), P < 0.001]. After surgery, flow complexity in the total patient cohort was reduced, but remained significantly higher compared to controls [median ES 2.3 (IQR 1.9-2.3) vs 1.0 (IQR 1.0-1.0), P < 0.001, median CFS 3.8 (IQR 3.0-4.3) vs 1.0 (IQR 1.0-2.0), P < 0.001]. In patients after mechanical AVR, flow complexity fell substantially and showed no difference from controls [median ES 1.0 (IQR 1.0-2.3) vs 1.0 (IQR 1.0-1.0), P = 0.46, median CFS 1.0 (IQR 1.0-3.3) vs 1.0 (IQR 1.0-2.0), P = 0.71]. In all 4 selected cases (biological, n = 2; mechanical, n = 2), model-based therapy planning reduced in silico complexity of flow profiles compared to the existing post-surgical findings [median ES 1.7 (IQR 1.4-1.7) vs 2.3 (IQR 2.3-2.3); CFS 1.7 (IQR 1.4-2.5) vs 3.8 (IQR 3.3-4.3)]. CONCLUSIONS: Abnormal flow profiles in the aorta more frequently persist after surgical AVR. Model-based therapy planning might have the potential to optimize treatment for best possible individual outcome. CLINICAL TRIAL REGISTRATION NUMBER: clinicaltrials.gov NCT03172338, 1 June 2017, retrospectively registered; NCT02591940, 30 October 2015, retrospectively registered.

Uncertainty Quantification for Non-invasive Assessment of Pressure Drop Across a Coarctation of the Aorta Using CFD.

PURPOSE: Numerical assessment of the pressure drop across an aortic coarctation using CFD is a promising approach to replace invasive catheter-based measurements. The aim of this study was to investigate and quantify the uncertainty of numerical calculation of the pressure drop introduced during two essential steps of medical image processing: segmentation of the patient-specific geometry and measurement of patient-specific flow rates from 4D-flow-MRI. METHODS: Based on the baseline segmentation, geometries with different stenosis diameters were generated for a sample of ten patients. The pressure drop generated by these geometries was calculated for different volume flow rates using computational fluid dynamics. Based on these simulations, a second order polynomial fit was calculated. Based on these polynomial fits an uncertainty of pressure drop calculation was quantified. RESULTS: The calculated pressure drop values varied strongly between the patients. In four patients, pressure drops above and below the clinical threshold of 20 mmHg were found. The median standard deviation of the pressure drop was 2.3 mmHg. The sensitivity of the pressure drop toward changes in the volume flow rate or the stenosis geometry varied between patients. CONCLUSION: The uncertainty of numerical pressure drop calculation introduced by uncertainties during image segmentation and measurement of volume flow rates was comparable to the uncertainty of pressure drop measurements using invasive catheterization. However, in some patients this uncertainty would have led to different treatment decision. Therefore, patient-specific uncertainty assessment might help to better understand the reliability of a numerically calculated biomarker as the pressure drop across an aortic coarctation.

Beyond Pressure Gradients: The Effects of Intervention on Heart Power in Aortic Coarctation.

BACKGROUND: In aortic coarctation, current guidelines recommend reducing pressure gradients that exceed given thresholds. From a physiological standpoint this should ideally improve the energy expenditure of the heart and thus prevent long term organ damage. OBJECTIVES: The aim was to assess the effects of interventional treatment on external and internal heart power (EHP, IHP) in patients with aortic coarctation and to explore the correlation of these parameters to pressure gradients obtained from heart catheterization. METHODS: In a collective of 52 patients with aortic coarctation 25 patients received stenting and/or balloon angioplasty, and 20 patients underwent MRI before and after an interventional treatment procedure. EHP and IHP were computed based on catheterization and MRI measurements. Along with the power efficiency these were combined in a cardiac energy profile. RESULTS: By intervention, the catheter gradient was significantly reduced from 21.8±9.4 to 6.2±6.1mmHg (p<0.001). IHP was significantly reduced after intervention, from 8.03±5.2 to 4.37±2.13W (p < 0.001). EHP was 1.1±0.3 W before and 1.0±0.3W after intervention, p = 0.044. In patients initially presenting with IHP above 5W intervention resulted in a significant reduction in IHP from 10.99±4.74 W to 4.94±2.45W (p<0.001), and a subsequent increase in power efficiency from 14 to 26% (p = 0.005). No significant changes in IHP, EHP or power efficiency were observed in patients initially presenting with IHP < 5W. CONCLUSION: It was demonstrated that interventional treatment of coarctation resulted in a decrease in IHP. Pressure gradients, as the most widespread clinical parameters in coarctation, did not show any correlation to changes in EHP or IHP. This raises the question of whether they should be the main focus in coarctation interventions. Only patients with high IHP of above 5W showed improvement in IHP and power efficiency after the treatment procedure. TRIAL REGISTRATION: clinicaltrials.gov NCT02591940.