Israel Valverde

euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling

The loss of cardiac pump function accounts for a significant increase in both mortality and morbidity in Western society, where there is currently a one in four lifetime risk, and costs associated with acute and long-term hospital treatments are accelerating. The significance of cardiac disease has motivated the application of state-of-the-art clinical imaging techniques and functional signal analysis to aid diagnosis and clinical planning. Measurements of cardiac function currently provide high-resolution datasets for characterizing cardiac patients. However, the clinical practice of using population-based metrics derived from separate image or signal-based datasets often indicates contradictory treatments plans owing to inter-individual variability in pathophysiology. To address this issue, the goal of our work, demonstrated in this study through four specific clinical applications, is to integrate multiple types of functional data into a consistent framework using multi-scale computational modelling.

Systemic-to-pulmonary collateral flow in patients with palliated univentricular heart physiology: measurement using cardiovascular magnetic resonance 4D velocity acquisition

BackgroundSystemic-to-pulmonary collateral flow (SPCF) may constitute a risk factor for increased morbidity and mortality in patients with single-ventricle physiology (SV). However, clinical research is limited by the complexity of multi-vessel two-dimensional (2D) cardiovascular magnetic resonance (CMR) flow measurements. We sought to validate four-dimensional (4D) velocity acquisition sequence for concise quantification of SPCF and flow distribution in patients with SV.Methods29 patients with SV physiology prospectively underwent CMR (1.5 T) (n = 14 bidirectional cavopulmonary connection [BCPC], age 2.9 ± 1.3 years; and n = 15 Fontan, 14.4 ± 5.9 years) and 20 healthy volunteers (age, 28.7 ± 13.1 years) served as controls. A single whole-heart 4D velocity acquisition and five 2D flow acquisitions were performed in the aorta, superior/inferior caval veins, right/left pulmonary arteries to serve as gold-standard. The five 2D velocity acquisition measurements were compared with 4D velocity acquisition for validation of individual vessel flow quantification and time efficiency. The SPCF was calculated by evaluating the disparity between systemic (aortic minus caval vein flows) and pulmonary flows (arterial and venour return). The pulmonary right to left and the systemic lower to upper body flow distribution were also calculated.ResultsThe comparison between 4D velocity and 2D flow acquisitions showed good Bland-Altman agreement for all individual vessels (mean bias, 0.05±0.24 l/min/m2), calculated SPCF (−0.02±0.18 l/min/m2) and significantly shorter 4D velocity acquisition-time (12:34 min/17:28 min,p < 0.01). 4D velocity acquisition in patients versus controls revealed (1) good agreement between systemic versus pulmonary estimator for SPFC; (2) significant SPCF in patients (BCPC 0.79±0.45 l/min/m2; Fontan 0.62±0.82 l/min/m2) and not in controls (0.01 + 0.16 l/min/m2), (3) inverse relation of right/left pulmonary artery perfusion and right/left SPCF (Pearson = −0.47,p = 0.01) and (4) upper to lower body flow distribution trend related to the weight (r = 0.742, p < 0.001) similar to the controls.Conclusions4D velocity acquisition is reliable, operator-independent and more time-efficient than 2D flow acquisition to quantify SPCF. There is considerable SPCF in BCPC and Fontan patients. SPCF was more pronounced towards the respective lung with less pulmonary arterial flow suggesting more collateral flow where less anterograde branch pulmonary artery perfusion.

Caval blood flow distribution in patients with Fontan circulation: quantification by using particle traces from 4D flow MR imaging.

PURPOSE To validate the use of particle traces derived from four-dimensional (4D) flow magnetic resonance (MR) imaging to quantify in vivo the caval flow contribution to the pulmonary arteries (PAs) in patients who had been treated with the Fontan procedure. MATERIALS AND METHODS The institutional review boards approved this study, and informed consent was obtained. Twelve healthy volunteers and 10 patients with Fontan circulation were evaluated. The particle trace method consists of creating a region of interest (ROI) on a blood vessel, which is used to emit particles with a temporal resolution of approximately 40 msec. The flow distribution, as a percentage, is then estimated by counting the particles arriving to different ROIs. To validate this method, two independent observers used particle traces to calculate the flow contribution of the PA to its branches in volunteers and compared it with the contribution estimated by measuring net forward flow volume (reference method). After the method was validated, caval flow contributions were quantified in patients. Statistical analysis was performed with nonparametric tests and Bland-Altman plots. P < .05 was considered to indicate a significant difference. RESULTS Estimation of flow contributions by using particle traces was equivalent to estimation by using the reference method. Mean flow contribution of the PA to the right PA in volunteers was 54% ± 3 (standard deviation) with the reference method versus 54% ± 3 with the particle trace method for observer 1 (P = .4) and 54% ± 4 versus 54% ± 4 for observer 2 (P = .6). In patients with Fontan circulation, 87% ± 13 of the superior vena cava blood flowed to the right PA (range, 63%-100%), whereas 55% ± 19 of the inferior vena cava blood flowed to the left PA (range, 22%-82%). CONCLUSION Particle traces derived from 4D flow MR imaging enable in vivo quantification of the caval flow distribution to the PAs in patients with Fontan circulation. This method might allow the identification of patients at risk of developing complications secondary to uneven flow distribution. SUPPLEMENTAL MATERIAL http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120778/-/DC1.

A systematic review of image segmentation methodology, used in the additive manufacture of patient-specific 3D printed models of the cardiovascular system.

Background Shortcomings in existing methods of image segmentation preclude the widespread adoption of patient-specific 3D printing as a routine decision-making tool in the care of those with congenital heart disease. We sought to determine the range of cardiovascular segmentation methods and how long each of these methods takes. Methods A systematic review of literature was undertaken. Medical imaging modality, segmentation methods, segmentation time, segmentation descriptive quality (SDQ) and segmentation software were recorded. Results Totally 136 studies met the inclusion criteria (1 clinical trial; 80 journal articles; 55 conference, technical and case reports). The most frequently used image segmentation methods were brightness thresholding, region growing and manual editing, as supported by the most popular piece of proprietary software: Mimics (Materialise NV, Leuven, Belgium, 1992–2015). The use of bespoke software developed by individual authors was not uncommon. SDQ indicated that reporting of image segmentation methods was generally poor with only one in three accounts providing sufficient detail for their procedure to be reproduced. Conclusions and implication of key findings Predominantly anecdotal and case reporting precluded rigorous assessment of risk of bias and strength of evidence. This review finds a reliance on manual and semi-automated segmentation methods which demand a high level of expertise and a significant time commitment on the part of the operator. In light of the findings, we have made recommendations regarding reporting of 3D printing studies. We anticipate that these findings will encourage the development of advanced image segmentation methods.

Dobutamine stress MRI in repaired tetralogy of Fallot with chronic pulmonary regurgitation: A comparison with healthy volunteers

BACKGROUND To compare the ventricular response to dobutamine stress between adult patients with chronic pulmonary regurgitation (PR) after repair of tetralogy of Fallot (r-TOF) and healthy volunteers using a staged dobutamine stress MR (DS-MR) protocol. METHODS Eighteen r-TOF patients (median age 31.9 years, range 16.2-60.1) with severe PR and 10 healthy controls (median age 40.6 years, range 23.9-51.8) completed staged DS-MR (baseline, 10 and 20 μg/kg/min) with ventricular volumetry and pulmonary flow quantification. Comparative analysis involved 3-way ANOVA, t-test, regression analysis, and coefficient of variance. RESULTS All controls had significant increase of ejection fraction (EF) at each stress level for both ventricles (normal contractile reserve, all p<0.05). In r-TOF patients (RV-EDV 126 ± 27 ml/m(2), RV-EF 55 ± 7%, LV-EF 58 ± 6%, PR-fraction 43 ± 15%), low-dose DS-MR at 10 μg/kg/min demonstrated normal biventricular contractile reserve as seen in volunteers. On increase from 10 to 20 μg/kg/min a subgroup showed worsening ejection fraction (n=8, p<0.05), mainly due to lack of reduction or even increase of RV-ESV, while the remainder responded with further reduction of RV-ESV and RV-EDV (n=10, p<0.05) and a non-significant trend to increased EF. This different response could not be predicted at baseline. CONCLUSIONS In r-TOF patients with chronic PR, DS-MR at 10 μg/kg/min showed normal biventricular systolic response compared with controls. Increase to 20 μg/kg/min provoked abnormal RV-ESV response in some r-TOF patients, suggesting presence of ventricular systolic dysfunction not evident at rest.

Three-dimensional Dual-Phase Whole-Heart MR Imaging: Clinical Implications for Congenital Heart Disease

PURPOSE To identify which rest phase (systolic or diastolic) is optimum for assessing or measuring cardiac structures in the setting of three-dimensional (3D) whole-heart imaging in congenital heart disease (CHD). MATERIALS AND METHODS The study was approved by the institutional review board; informed consent was obtained. Fifty children (26 male and 24 female patients) underwent 3D dual-phase whole-heart imaging. Cardiac structures were analyzed for contrast-to-noise ratio (CNR) and image quality. Cross-sectional measurements were taken of the aortic arch, right ventricular (RV) outflow tract (RVOT) and pulmonary arteries. Normally distributed variables were compared by using paired t tests, and categorical data were compared by using Wilcoxon signed-rank test. RESULTS Mean CNR and image quality were significantly (all P < .05) greater in systole for the right atrium (CNR, 8.9 vs 7.5; image quality, 438 vs 91), left atrium (CNR, 8.0 vs 5.3; image quality, 1006 vs 29), RV (CNR, 10.6 vs 8.2; image quality, 131 vs 23), LV (CNR, 9.4 vs 7.7; image quality, 125 vs 28), and pulmonary veins (CNR, 6.2 vs 4.9; image quality, 914 vs 32). Conversely, diastolic CNR was significantly higher in the aorta (9.2 vs 8.2; P = .013) and diastolic image quality was higher for the left pulmonary artery (238 vs 62; P = .007), right pulmonary artery (219 vs 35; P < .001), and for imaging of an area after an arterial stenosis (164 vs 7; P < .001). All aortic arch and RVOT cross-sectional measurements were significantly (P < .05) greater in systole (narrowest point of arch, 70 vs 53 mm(2); descending aorta, 71 vs 58 mm(2); transverse arch, 293 vs 275 mm(2); valvar RVOT, 291 vs 268 mm(2); supravalvar RVOT, 337 vs 280 mm(2); prebifurcation RVOT, 329 vs 259 mm(2)). CONCLUSION Certain structures in CHD are better imaged in systole and others in diastole, and therefore, the dual-phase approach allows a higher overall success rate. This approach also allows depiction of diameter changes between systole and diastole and is therefore preferable to standard single-phase sequences for the planning of interventional procedures.

3D Quantification of Wall Shear Stress and Oscillatory Shear Index Using a Finite-Element Method in 3D CINE PC-MRI Data of the Thoracic Aorta

Several 2D methods have been proposed to estimate WSS and OSI from PC-MRI, neglecting the longitudinal velocity gradients that typically arise in cardiovascular flow, particularly on vessel geometries whose cross section and centerline orientation strongly vary in the axial direction. Thus, the contribution of longitudinal velocity gradients remains understudied. In this work, we propose a 3D finite-element method for the quantification of WSS and OSI from 3D-CINE PC-MRI that accounts for both in-plane and longitudinal velocity gradients. We demonstrate the convergence and robustness of the method on cylindrical geometries using a synthetic phantom based on the Poiseuille flow equation. We also show that, in the presence of noise, the method is both stable and accurate. Using computational fluid dynamics simulations, we show that the proposed 3D method results in more accurate WSS estimates than those obtained from a 2D analysis not considering out-of-plane velocity gradients. Further, we conclude that for irregular geometries the accurate prediction of WSS requires the consideration of longitudinal gradients in the velocity field. Additionally, we compute 3D maps of WSS and OSI for 3D-CINE PC-MRI data sets from an aortic phantom and sixteen healthy volunteers and two patients. The OSI values show a greater dispersion than WSS, which is strongly dependent on the PC-MRI resolution. We envision that the proposed 3D method will improve the estimation of WSS and OSI from 3D-CINE PC-MRI images, allowing for more accurate estimates in vessels with pathologies that induce high longitudinal velocity gradients, such as coarctations and aneurisms.

Identification of artery wall stiffness: in vitro validation and in vivo results of a data assimilation procedure applied to a 3D fluid-structure interaction model

We consider the problem of estimating the stiffness of an artery wall using a data assimilation method applied to a 3D fluid-structure interaction (FSI) model. Recalling previous works, we briefly present the FSI model, the data assimilation procedure and the segmentation algorithm. We present then two examples of the procedure using real data. First, we estimate the stiffness distribution of a silicon rubber tube from image data. Second, we present the estimation of aortic wall stiffness from real clinical data.

Generating anatomical models of the heart and the aorta from medical images for personalized physiological simulations

The anatomy and motion of the heart and the aorta are essential for patient-specific simulations of cardiac electrophysiology, wall mechanics and hemodynamics. Within the European integrated project euHeart, algorithms have been developed that allow to efficiently generate patient-specific anatomical models from medical images from multiple imaging modalities. These models, for instance, account for myocardial deformation, cardiac wall motion, and patient-specific tissue information like myocardial scar location. Furthermore, integration of algorithms for anatomy extraction and physiological simulations has been brought forward. Physiological simulations are linked closer to anatomical models by encoding tissue properties, like the muscle fibers, into segmentation meshes. Biophysical constraints are also utilized in combination with image analysis to assess tissue properties. Both examples show directions of how physiological simulations could provide new challenges and stimuli for image analysis research in the future.

Quantification of wall shear stress using a finite-element method in multidimensional phase-contrast MR data of the thoracic aorta

We present a computational method for calculating the distribution of wall shear stress (WSS) in the aorta based on a velocity field obtained from two-dimensional (2D) phase-contrast magnetic resonance imaging (PC-MRI) data and a finite-element method. The WSS vector was obtained from a global least-squares stress-projection method. The method was benchmarked against the Womersley model, and the robustness was assessed by changing resolution, noise, and positioning of the vessel wall. To showcase the applicability of the method, we report the axial, circumferential and magnitude of the WSS using in-vivo data from five volunteers. Our results showed that WSS values obtained with our method were in good agreement with those obtained from the Womersley model. The results for the WSS contour means showed a systematic but decreasing bias when the pixel size was reduced. The proposed method proved to be robust to changes in noise level, and an incorrect position of the vessel wall showed large errors when the pixel size was decreased. In volunteers, the results obtained were in good agreement with those found in the literature. In summary, we have proposed a novel image-based computational method for the estimation of WSS on vessel sections with arbitrary cross-section geometry that is robust in the presence of noise and boundary misplacements.