Examinando por Autor "Sotelo, Julio"

Mostrando 1 - 10 de 10
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    A comprehensive comparison between shortest-path HARP refinement, SinMod, and DENSEanalysis processing tools applied to CSPAMM and DENSE images
    (Elsevier, 2021) Mella, Hernán; Mura, Joaquín; Sotelo, Julio; Uribe, Sergio
    We addressed comprehensively the performance of Shortest-Path HARP Refinement (SP-HR), SinMod, and DENSEanalysis using 2D slices of synthetic CSPAMM and DENSE images with realistic contrasts obtained from 3D phantoms. The three motion estimation techniques were interrogated under ideal and no-ideal conditions (with MR induced artifacts, noise, and through-plane motion), considering several resolutions and noise levels. Under noisy conditions, and for isotropic pixel sizes of 1.5 mm and 3.0 mm in CSPAMM and DENSE images respectively, the nRMSE obtained for the circumferential and radial strain components were 10.7 ± 10.8% and 25.5 ± 14.8% using SP-HR, 11.9 ± 2.5% and 29.3 ± 6.5% using SinMod, and 6.4 ± 2.0% and 18.2 ± 4.6% using DENSEanalysis. Overall, the results showed that SP-HR tends to fail for large tissue motions, whereas SinMod and DENSEanalysis gave accurate displacement and strain field estimations, being the last which performed the best.
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    Abnormal vortex formation in the right pulmonary artery after the arterial switch operation
    (European Society Of Cardiology, 2021) Warmerdam, Evangeline G.; Van Assen, Hans C.; Sotelo, Julio; Grotenhuis, Heynric B.
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    Altered Aortic Hemodynamics and Relative Pressure in Patients with Dilated Cardiomyopathy
    (Springer, 2022) Marlevi, David; Mariscal‐Harana, Jorge; Burris, Nicholas S.; Sotelo, Julio; Ruijsink, Bram; Hadjicharalambous, Myrianthi; Asner, Liya; Sammut, Eva; Chabiniok, Radomir; Uribe, Sergio; Winter, Reidar; Lamata, Pablo; Alastruey, Jordi; Nordsletten, David
    Ventricular-vascular interaction is central in the adaptation to cardiovascular disease. However, cardiomyopathy patients are predominantly monitored using cardiac biomarkers. The aim of this study is therefore to explore aortic function in dilated cardiomyopathy (DCM). Fourteen idiopathic DCM patients and 16 controls underwent cardiac magnetic resonance imaging, with aortic relative pressure derived using physics-based image processing and a virtual cohort utilized to assess the impact of cardiovascular properties on aortic behaviour. Subjects with reduced left ventricular systolic function had significantly reduced aortic relative pressure, increased aortic stiffness, and significantly delayed time-to-pressure peak duration. From the virtual cohort, aortic stiffness and aortic volumetric size were identified as key determinants of aortic relative pressure. As such, this study shows how advanced flow imaging and aortic hemodynamic evaluation could provide novel insights into the manifestation of DCM, with signs of both altered aortic structure and function derived in DCM using our proposed imaging protocol.
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    Comprehensive Assessment of Left Intraventricular Hemodynamics Using a Finite Element Method: An Application to Dilated Cardiomyopathy Patients
    (MDPI, 2021) Franco, Pamela; Sotelo, Julio; Montalba, Cristian; Ruijsink, Bram; Kerfoot, Eric; Nordsletten, David; Mura, Joaquín; Hurtado, Daniel; Uribe, Sergio
    In this paper, we applied a method for quantifying several left intraventricular hemodynamic parameters from 4D Flow data and its application in a proof-of-concept study in dilated cardiomyopathy (DCM) patients. In total, 12 healthy volunteers and 13 DCM patients under treatment underwent short-axis cine b-SSFP and 4D Flow MRI. Following 3D segmentation of the left ventricular (LV) cavity and registration of both sequences, several hemodynamic parameters were calculated at peak systole, e-wave, and end-diastole using a finite element approach. Sensitivity, inter- and intra-observer reproducibility of hemodynamic parameters were evaluated by analyzing LV segmentation. A local analysis was performed by dividing the LV cavity into 16 regions. We found significant differences between volunteers and patients in velocity, vorticity, viscous dissipation, energy loss, and kinetic energy at peak systole and e-wave. Furthermore, although five patients showed a recovered ejection fraction after treatment, their hemodynamic parameters remained low. We obtained several hemodynamic parameters with high inter- and intra-observer reproducibility. The sensitivity study revealed that hemodynamic parameters showed a higher accuracy when the segmentation underestimates the LV volumes. Our approach was able to identify abnormal flow patterns in DCM patients compared to volunteers and can be applied to any other cardiovascular diseases.
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    HARP-I: A Harmonic Phase Interpolation Method for the Estimation of Motion From Tagged MR Images
    (IEEE, 2021) Mella, Hernán; Mura, Joaquín; Wang, Hui; Taylor, Michael D.; Chabiniok, Radomir; Tintera, Jaroslav; Sotelo, Julio; Uribe, Sergio
    We proposed a novel method called HARP-I, which enhances the estimation of motion from tagged Magnetic Resonance Imaging (MRI). The harmonic phase of the images is unwrapped and treated as noisy measurements of reference coordinates on a deformed domain, obtaining motion with high accuracy using Radial Basis Functions interpolations. Results were compared against Shortest Path HARP Refinement (SP-HR) and Sine-wave Modeling (SinMod), two harmonic image-based techniques for motion estimation from tagged images. HARP-I showed a favorable similarity with both methods under noise-free conditions, whereas a more robust performance was found in the presence of noise. Cardiac strain was better estimated using HARP-I at almost any motion level, giving strain maps with less artifacts. Additionally, HARP-I showed better temporal consistency as a new method was developed to fix phase jumps between frames. In conclusion, HARP-I showed to be a robust method for the estimation of motion and strain under ideal and non-ideal conditions.
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    Identification of hemodynamic biomarkers for bicuspid aortic valve induced aortic dilation using machine learning
    (Elsevier, 2022) Franco, Pamela; Sotelo, Julio; Guala; Dux-Santo, Lydia; Evangelista, Arturo; Rodríguez-Palomares, José; Mery, Domingo; Salas.Rodrigo; Uribe, Sergio
    Recent advances in medical imaging have confirmed the presence of altered hemodynamics in bicuspid aortic valve (BAV) patients. Therefore, there is a need for new hemodynamic biomarkers to refine disease monitoring and improve patient risk stratification. This research aims to analyze and extract multiple correlation patterns of hemodynamic parameters from 4D Flow MRI data and find which parameters allow an accurate classification between healthy volunteers (HV) and BAV patients with dilated and non-dilated ascending aorta using machine learning. Sixteen hemodynamic parameters were calculated in the ascending aorta (AAo) and aortic arch (AArch) at peak systole from 4D Flow MRI. We used sequential forward selection (SFS) and principal component analysis (PCA) as feature selection algorithms. Then, eleven machine-learning classifiers were implemented to separate HV and BAV patients (non- and dilated ascending aorta). Multiple correlation patterns from hemodynamic parameters were extracted using hierarchical clustering. The linear discriminant analysis and random forest are the best performing classifiers, using five hemodynamic parameters selected with SFS (velocity angle, forward velocity, vorticity, and backward velocity in AAo; and helicity density in AArch) a 96.31 ± 1.76% and 96.00 ± 0.83% accuracy, respectively. Hierarchical clustering revealed three groups of correlated features. According to this analysis, we observed that features selected by SFS have a better performance than those selected by PCA because the five selected parameters were distributed according to 3 different clusters. Based on the proposed method, we concluded that the feature selection method found five potentially hemodynamic biomarkers related to this disease.
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    Impact of aortic arch curvature in flow haemodynamics in patients with transposition of the great arteries after arterial switch operation
    (European Society Of Cardiology, 2022) Sotelo, Julio; Valverde, Israel; Martins, Duarte; Bonnet, Damien; Boddaert, Nathalie; Pushparajan, Kuberan; Uribe, Sergio; Raimondi, Francesca
    Aims. In this study, we will describe a comprehensive haemodynamic analysis and its relationship to the dilation of the aorta in transposition of the great artery (TGA) patients post-arterial switch operation (ASO) and controls using 4D-flow magnetic resonance imaging (MRI) data. Methods and results. Using 4D-flow MRI data of 14 TGA young patients and 8 age-matched normal controls obtained with 1.5 T GE-MR scanner, we evaluate 3D maps of 15 different haemodynamics parameters in six regions; three of them in the aortic root and three of them in the ascending aorta (anterior-left, -right, and posterior for both cases) to find its relationship with the aortic arch curvature and root dilation. Differences between controls and patients were evaluated using Mann–Whitney U test, and the relationship with the curvature was accessed by unpaired t-test. For statistical significance, we consider a P-value of 0.05. The aortic arch curvature was significantly different between patients 46.238 ± 5.581 m−1 and controls 41.066 ± 5.323 m−1. Haemodynamic parameters as wall shear stress circumferential (WSS-C), and eccentricity (ECC), were significantly different between TGA patients and controls in both the root and ascending aorta regions. The distribution of forces along the ascending aorta is highly inhomogeneous in TGA patients. We found that the backward velocity (B-VEL), WSS-C, velocity angle (VEL-A), regurgitation fraction (RF), and ECC are highly correlated with the aortic arch curvature and root dilatation. Conclusion. We have identified six potential biomarkers (B-VEL, WSS-C, VEL-A, RF, and ECC), which may be helpful for follow-up evaluation and early prediction of aortic root dilatation in this patient population.
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    Non-invasive local pulse wave velocity using 4D-flow MRI
    (Elsevier, 2021) Mura, Joaquín; Sotelo, Julio; Mella, Hernan; Wong, James; Hussain, Tarique; Ruijsink, Bram; Uribe, Sergio
    Pulse Wave Velocity (PWV) corresponds to the velocity at which pressure waves, generated by the systolic contraction in the heart, propagate along the arterial tree. Due to the complex interplay between blood flow and the artery wall, PWV is related to inherent mechanical properties and arterial morphology. PWV has been widely accepted as a biomarker and early predictor to evaluate global arterial distensibility. Still, several local abnormalities often remain hidden or difficult to detect using non-invasive techniques. Here, we introduce a novel method to efficiently construct a local estimate of PWV along the aorta using 4D-Flow MRI data. A geodesic distance map was used to track advancing pulses for efficient flow calculations, based on the observation that the propagation of velocity wavefronts strongly depends on the arterial morphology. This procedure allows us a robust evaluation of the local transit time due to the pulse wave at each position in the aorta. Moreover, the estimation of the local PWV map did not require centerlines, and the final result is projected back to 3D using the same geodesic map. We evaluated PWV values in healthy young and adult volunteers and patients with univentricular physiology after a Fontan procedure. Our method is fast, semi-automatic, and depicts differences between young versus adult volunteers and young volunteers versus Fontan patients, showing consistent results compared to global methods. Remarkably, the technique could detect local differences of PWV on the aortic arch for all subjects, being consistent with previous findings of reduced PWV in the aortic arch.
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    Three-dimensional quantification of circulation using finite-element methods in four-dimensional flow MR data of the thoracic aorta
    (Wiley, 2022) Sotelo, Julio; Bissell, Malenka M.; Jiang, Yaxin; Mella, Hernan; Mura, Joaquín; Uribe, Sergio
    Purpose. Three-dimensional (3D) quantification of circulation using a Finite Elements methodology. Methods. We validate our 3D method using an in-silico arch model, for different mesh resolutions, image resolution and noise levels, and we compared this with a currently used 2D method. Finally, we evaluated the application of our methodology in 4D Flow MRI data of ascending aorta of six healthy volunteers, and six bicuspid aortic valve (BAV) patients, three with right and three with left handed flow, at peak systole. The in-vivo data was compared using a Mann-Whitney U-test between volunteers and patients (right and left handed flow). Results.The robustness of our method throughout different image resolutions and noise levels showed subestimation of circulation less than 45 cm2/s in comparison with the 55cm2/s generated by the current 2D method. The circulation (mean ± SD) of the healthy volunteer group was 13.83 ± 28.78 cm2/s, in BAV patients with right-handed flow 724.37 ± 317.53 cm2/s, and BAV patients with left-handed flow −480.99 ± 387.29 cm2/s. There were significant differences between healthy volunteers and BAV patients groups (P-value < .01), and also between BAV patients with a right-handed or left-handed helical flow and healthy volunteers (P-value < .01). Conclusion. We propose a novel 3D formulation to estimate the circulation in the thoracic aorta, which can be used to assess the differences between normal and diseased hemodynamic from 4D-Flow MRI data. This method also can correctly differentiate between the visually seen right- and left-handed helical flow, which suggests that this approach may have high clinical sensitivity, but requires confirmation in longitudinal studies with a large cohort.
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    Validation of 4D Flow based relative pressure maps in aortic flows
    (Elsevier, 2021) Noltea, David; Urbina, Jesús; Sotelo, Julio; Sok, Leo; Montalba, Cristian; Valverde, Israel; Osses, Axel; Uribe, Sergio; Bertoglio, Cristóbal
    While the clinical gold standard for pressure difference measurements is invasive catheterization, 4D Flow MRI is a promising tool for enabling a non-invasive quantification, by linking highly spatially resolved velocity measurements with pressure differences via the incompressible Navier–Stokes equations. In this work we provide a validation and comparison with phantom and clinical patient data of pressure difference maps estimators. We compare the classical Pressure Poisson Estimator (PPE) and the new Stokes Estimator (STE) against catheter pressure measurements under a variety of stenosis severities and flow intensities. Specifically, we use several 4D Flow data sets of realistic aortic phantoms with different anatomic and hemodynamic severities and two patients with aortic coarctation. The phantom data sets are enriched by subsampling to lower resolutions, modification of the segmentation and addition of synthetic noise, in order to study the sensitivity of the pressure difference estimators to these factors. Overall, the STE method yields more accurate results than the PPE method compared to catheterization data. The superiority of the STE becomes more evident at increasing Reynolds numbers with a better capacity of capturing pressure gradients in strongly convective flow regimes. The results indicate an improved robustness of the STE method with respect to variation in lumen segmentation. However, with heuristic removal of the wall-voxels, the PPE can reach a comparable accuracy for lower Reynolds’ numbers.