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Severity of aortic coarctation (CoA) is currently assessed by estimating trans-coarctation pressure drops through cardiac catheterization or echocardiography. In principle, more detailed information could be obtained non-invasively based on space- and time-resolved magnetic resonance imaging (4D flow) data. Yet the limitations of this imaging technique require testing the accuracy of 4D flow-derived hemodynamic quantities against other methodologies. With the objective of assessing the feasibility and accuracy of this non-invasive method to support the clinical diagnosis of CoA, we developed an algorithm (4DF-FEPPE) to obtain relative pressure distributions from 4D flow data by solving the Poisson pressure equation. 4DF-FEPPE was tested against results from a patient-specific fluid-structure interaction (FSI) simulation, whose patient-specific boundary conditions were prescribed based on 4D flow data. Since numerical simulations provide noise-free pressure fields on fine spatial and temporal scales, our analysis allowed to assess the uncertainties related to 4D flow noise and limited resolution. 4DF-FEPPE and FSI results were compared on a series of cross-sections along the aorta. Bland-Altman analysis revealed very good agreement between the two methodologies in terms of instantaneous data at peak systole, end-diastole and time-averaged values: biases (means of differences) were +0.4 mmHg, -1.1 mmHg and +0.6 mmHg, respectively. Limits of agreement (2 SD) were ±0.978 mmHg, ±1.06 mmHg and ±1.97 mmHg, respectively. Peak-to-peak and maximum trans-coarctation pressure drops obtained with 4DF-FEPPE differed from FSI results by 0.75 mmHg and -1.34 mmHg respectively. The present study considers important validation aspects of non-invasive pressure difference estimation based on 4D flow MRI, showing the potential of this technology to be more broadly applied to the clinical practice., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The performance of blood-processing devices largely depends on the associated fluid dynamics, which hence represents a key aspect in their design and optimization. To this aim, two approaches are currently adopted: computational fluid-dynamics, which yields highly resolved three-dimensional data but relies on simplifying assumptions, and in vitro experiments, which typically involve the direct video-acquisition of the flow field and provide 2D data only. We propose a novel method that exploits space- and time-resolved magnetic resonance imaging (4D-flow) to quantify the complex 3D flow field in blood-processing devices and to overcome these limitations. We tested our method on a real device that integrates an oxygenator and a heat exchanger. A dedicated mock loop was implemented, and novel 4D-flow sequences with sub-millimetric spatial resolution and region-dependent velocity encodings were defined. Automated in house software was developed to quantify the complex 3D flow field within the different regions of the device: region-dependent flow rates, pressure drops, paths of the working fluid and wall shear stresses were computed. Our analysis highlighted the effects of fine geometrical features of the device on the local fluid-dynamics, which would be unlikely observed by current in vitro approaches. Also, the effects of non-idealities on the flow field distribution were captured, thanks to the absence of the simplifying assumptions that typically characterize numerical models. To the best of our knowledge, our approach is the first of its kind and could be extended to the analysis of a broad range of clinically relevant devices., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Objectives: Finite element modeling was adopted to quantitatively compare, for the first time and on a patient-specific basis, the biomechanical effects of a broad spectrum of different neochordal implantation techniques for the repair of isolated posterior mitral leaflet prolapse., Methods: Cardiac magnetic resonance images were acquired from 4 patients undergoing surgery. A patient-specific 3-dimensional model of the mitral apparatus and the motion of the annulus and papillary muscles were reconstructed. The location and extent of the prolapsing region were confirmed by intraoperative findings, and the mechanical properties of the mitral leaflets, chordae tendineae and expanded polytetrafluoroethylene neochordae were included. Mitral systolic biomechanics was simulated under preoperative conditions and after 5 different neochordal procedures: single neochorda, double neochorda, standard neochordal loop with 3 neochordae of the same length and 2 premeasured loops with 1 common neochordal loop and 3 different branched neochordae arising from it, alternatively one third and two thirds of the entire length., Results: The best repair in terms of biomechanics was achieved with a specific neochordal technique in the single patient, according to the location of the prolapsing region. However, all techniques achieved a slight reduction in papillary muscle forces and tension relief in intact native chordae proximal to the prolapsing region. Multiple neochordae implantation improved the repositioning of the prolapsing region below the annular plane and better redistributed mechanical stresses on the leaflet., Conclusions: Although applied on a small cohort of patients, systematic biomechanical differences were noticed between neochordal techniques, potentially affecting their short- to long-term clinical outcomes. This study opens the way to patient-specific optimization of neochordal techniques., (Copyright © 2014 The American Association for Thoracic Surgery. Published by Mosby, Inc. All rights reserved.)

An understanding of aortic root biomechanics is pivotal for the optimisation of surgical procedures aimed at restoring normal root function in pathological subjects. For this purpose, computational models can provide important information, as long as they realistically capture the main anatomical and functional features of the aortic root. Here we present a novel and realistic finite element (FE) model of the physiological aortic root, which simulates its function during the entire cardiac cycle. Its geometry is based on magnetic resonance imaging (MRI) data obtained from 10 healthy subjects and accounts for the geometrical differences between the leaflet-sinus units. Morphological realism is combined with the modelling of the leaflets' non-linear and anisotropic mechanical response, in conjunction with dynamic boundary conditions. The results show that anatomical differences between leaflet-sinus units cause differences in stress and strain patterns. These are notably higher for the leaflets and smaller for the sinuses. For the maximum transvalvular pressure value, maximum principal stresses on the leaflets are equal to 759, 613 and 603 kPa on the non-coronary, right and left leaflet, respectively. For the maximum aortic pressure, average maximum principal stresses values are equal to 118, 112 and 111 kPa on the right, non-coronary and left sinus, respectively. Although liable of further improvements, the model seems to reliably reproduce the behaviour of the real aortic root: the model's leaflet stretches, leaflet coaptation lengths and commissure motions, as well as the timings of aortic leaflet closures and openings, all matched with the experimental findings reported in the literature., ((c) 2009 IPEM. Published by Elsevier Ltd. All rights reserved.)

Understanding aortic root in vivo biomechanics can help in elucidating key mechanisms involved in aortic root pathologies and in the outcome of their surgical treatment. Numerical models can provide useful quantitative information. For this to be reliable, detailed aortic root anatomy should be captured. Also, since the aortic root is never unloaded throughout the cardiac cycle, the modeled geometry should be consistent with the in vivo loads acting on it. Achieving such consistency is still a challenge, which was tackled only by few numerical studies. Here we propose and describe in detail a new approach to the finite element modeling of aortic root in vivo structural mechanics. Our approach exploits the anatomical information yielded by magnetic resonance imaging by reconstructing the 3-dimensional end-diastolic geometry of the aortic root and makes the reconstructed geometry consistent with end-diastolic loading conditions through the estimation of the corresponding prestresses field. We implemented our approach through a semiautomated modeling pipeline, and we applied it to quantify aortic root biomechanics in 4 healthy participants. Computed results highlighted that including prestresses into the model allowed for pressurizing the aortic root to the end-diastolic pressure while matching the image-based ground truth data. Aortic root dynamics, tissues strains, and stresses computed at relevant time points through the cardiac cycle were consistent with a broad set of data from previous computational and in vivo studies, strongly suggesting the potential of the method. Also, results highlighted the major role played by the anatomy in driving aortic root biomechanics. [ABSTRACT FROM AUTHOR]

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Bicuspid aortic valve (BAV), i.e. the fusion of two aortic valve cusps, is the most frequent congenital cardiac malformation. Its progression is often characterized by accelerated leaflet calcification and aortic wall dilation. These processes are likely enhanced by altered biomechanical stimuli, including fluid-dynamic wall shear stresses (WSS) acting on both the aortic wall and the aortic valve. Several studies have proposed the exploitation of 4D-flow magnetic resonance imaging sequences to characterize abnormal in vivo WSS in BAV-affected patients, to support prognosis and timing of intervention. However, current methods fail to quantify WSS peak values. On this basis, we developed two new methods for the improved quantification of in vivo WSS acting on the aortic wall based on 4D-flow data. We tested both methods separately and in combination on synthetic datasets obtained by two computational fluid-dynamics (CFD) models of the aorta with healthy and bicuspid aortic valve. Tests highlighted the need for data spatial resolution at least comparable to current clinical guidelines, the low sensitivity of the methods to data noise, and their capability, when used jointly, to compute more realistic peak WSS values as compared to state-of-the-art methods. The integrated application of the two methods on the real 4D-flow data from a preliminary cohort of three healthy volunteers and three BAV-affected patients confirmed these indications. In particular, quantified WSS peak values were one order of magnitude higher than those reported in previous 4D-flow studies, and much closer to those computed by highly time- and space-resolved CFD simulations. [ABSTRACT FROM AUTHOR]