Deep Learning-based Automated Aortic Area and Distensibility Assessment: The Multi-Ethnic Study of Atherosclerosis (MESA)

This study applies convolutional neural network (CNN)-based automatic segmentation and distensibility measurement of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging (PC-cine MRI) within the large MESA cohort with subsequent assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. 2D PC-cine MRI images of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study were included. Train, validation, and internal test sets consisted of 1123 studies (24282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. An external test set of TAAs consisted of 37 studies (3224 images). A U-Net based CNN was constructed, and performance was evaluated utilizing dice coefficient (for segmentation) and concordance correlation coefficients (CCC) of aortic geometric parameters by comparing to manual segmentation and parameter estimation. Dice coefficients for aorta segmentation were 97.6% (CI: 97.5%-97.6%) and 93.6% (84.6%-96.7%) on the internal and external test of TAAs, respectively. CCC for comparison of manual and CNN maximum and minimum ascending aortic areas were 0.97 and 0.95, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. CCCs for maximum and minimum descending aortic areas were 0.96 and 0. 98, respectively, on the internal test set and 0.93 and 0.93, respectively, on the external test set. We successfully developed and validated a U-Net based ascending and descending aortic segmentation and distensibility quantification model in a large multi-ethnic database and in an external cohort of TAA patients.Comment: 25 pages, 5 figure

Apport de la mécanique des fluides dans l'étude des flux sanguins aortiques

Aging is associated with morphological, functional and hemodynamic changes in the arterial system, most often aggravated by cardiovascular disease. Understanding these aggravating interactions is important to reduce patients risk. Medical imaging plays a major role in this context through modalities such as velocity encoding MRI combined with quantitative image processing and computational resolution of Navier-Stokes equations that govern blood flow hemodynamics. The aim of this thesis is to develop and combine image processing methods dedicated to 4D flow MRI data analysis with computational fluid dynamics to extract quantitative biomarkers such as intra-aortic pressure fields and their spatio-temporal propagations, aortic wall shear stress and intra-aortic vorticity. We have demonstrated the ability of these biomarkers to detect age-related sub-clinical aortic impairment and to characterize pathological aortic dilatation. In addition, association of spatio-temporal aortic pressure distributions with vortex occurrence and duration as well as with wall shear stress were studied. In a second work, we developed a numerical simulation software to solve the Navier-Stokes system using finite element models. An iterative projection method was applied to 2D and 3D vessel stenosis models as well as to 3D geometrical aortic models resulting from segmentation to validate our implementation. Finally, a preliminary work applying our numerical model to patient-specific geometries was performed revealing encouraging associations between simulated data and MRI measures.Le vieillissement est associé à des modifications morphologiques, fonctionnelles et hémodynamiques du système artériel, le plus souvent aggravées par la survenue de maladies cardiovasculaires. La compréhension de ces interactions aggravantes est importante pour réduire le risque encouru par le patient. L’imagerie médicale joue un rôle majeur dans cette perspective au travers de modalités telles que l’IRM de contraste de phase combinée à l’analyse quantitative des images obtenues ainsi qu’à la résolution numérique des équations de Navier Stokes qui régissent l’hémodynamique de l’écoulement sanguin. Cette thèse a donc pour but de mettre au point et combiner des méthodes de traitement d’images de vélocimétrie 4D acquises en IRM et de mécanique des fluides pour extraire des biomarqueurs quantitatifs tels que les cartographies de pressions intra-aortiques et leurs propagations spatio-temporelles, la contrainte de cisaillement aux parois aortiques et la vorticité intra-aortique. Nous avons ainsi montré la capacité de ces biomarqueurs à détecter les atteintes infra-cliniques liées à l’âge et à caractériser la dilatation aortique pathologique. De plus, les liens entre distributions spatio-temporelles des pressions et apparition et persistance des vortex ou encore contrainte de cisaillement ont été montrés. Dans un second travail, nous avons mis au point un modèle de simulation numérique permettant de résoudre le système d’équations de Navier-Stokes par élément finis. Une méthode de projection itérative a été appliquée à des modèles de sténose 2D et 3D ainsi qu’à des géométries aortiques 3D issues de segmentations pour valider notre implémentation. Finalement, un travail préliminaire d’application de notre modèle numérique à des géométries patients-spécifiques a été réalisé indiquant des liens encourageants entre données simulées et mesures IRM

Contribution of fluid mechanics in the study of aortic blood flows

Le vieillissement est associé à des modifications morphologiques, fonctionnelles et hémodynamiques du système artériel, le plus souvent aggravées par la survenue de maladies cardiovasculaires. La compréhension de ces interactions aggravantes est importante pour réduire le risque encouru par le patient. L’imagerie médicale joue un rôle majeur dans cette perspective au travers de modalités telles que l’IRM de contraste de phase combinée à l’analyse quantitative des images obtenues ainsi qu’à la résolution numérique des équations de Navier Stokes qui régissent l’hémodynamique de l’écoulement sanguin. Cette thèse a donc pour but de mettre au point et combiner des méthodes de traitement d’images de vélocimétrie 4D acquises en IRM et de mécanique des fluides pour extraire des biomarqueurs quantitatifs tels que les cartographies de pressions intra-aortiques et leurs propagations spatio-temporelles, la contrainte de cisaillement aux parois aortiques et la vorticité intra-aortique. Nous avons ainsi montré la capacité de ces biomarqueurs à détecter les atteintes infra-cliniques liées à l’âge et à caractériser la dilatation aortique pathologique. De plus, les liens entre distributions spatio-temporelles des pressions et apparition et persistance des vortex ou encore contrainte de cisaillement ont été montrés. Dans un second travail, nous avons mis au point un modèle de simulation numérique permettant de résoudre le système d’équations de Navier-Stokes par élément finis. Une méthode de projection itérative a été appliquée à des modèles de sténose 2D et 3D ainsi qu’à des géométries aortiques 3D issues de segmentations pour valider notre implémentation. Finalement, un travail préliminaire d’application de notre modèle numérique à des géométries patients-spécifiques a été réalisé indiquant des liens encourageants entre données simulées et mesures IRM.Aging is associated with morphological, functional and hemodynamic changes in the arterial system, most often aggravated by cardiovascular disease. Understanding these aggravating interactions is important to reduce patients risk. Medical imaging plays a major role in this context through modalities such as velocity encoding MRI combined with quantitative image processing and computational resolution of Navier-Stokes equations that govern blood flow hemodynamics. The aim of this thesis is to develop and combine image processing methods dedicated to 4D flow MRI data analysis with computational fluid dynamics to extract quantitative biomarkers such as intra-aortic pressure fields and their spatio-temporal propagations, aortic wall shear stress and intra-aortic vorticity. We have demonstrated the ability of these biomarkers to detect age-related sub-clinical aortic impairment and to characterize pathological aortic dilatation. In addition, association of spatio-temporal aortic pressure distributions with vortex occurrence and duration as well as with wall shear stress were studied. In a second work, we developed a numerical simulation software to solve the Navier-Stokes system using finite element models. An iterative projection method was applied to 2D and 3D vessel stenosis models as well as to 3D geometrical aortic models resulting from segmentation to validate our implementation. Finally, a preliminary work applying our numerical model to patient-specific geometries was performed revealing encouraging associations between simulated data and MRI measures

Aortic Stiffness Measured from Either 2D/4D Flow and Cine MRI or Applanation Tonometry in Coronary Artery Disease: A Case–Control Study

International audienceBackground and objective: Aortic stiffness can be evaluated by aortic distensibility or pulse wave velocity (PWV) using applanation tonometry, 2D phase contrast (PC) MRI and the emerging 4D flow MRI. However, such MRI tools may reach their technical limitations in populations with cardiovascular disease. Accordingly, this work focuses on the diagnostic value of aortic stiffness evaluated either by applanation tonometry or MRI in high-risk coronary artery disease (CAD) patients. Methods: 35 patients with a multivessel CAD and a myocardial infarction treated 1 year before were prospectively recruited and compared with 18 controls with equivalent age and sex distribution. Ascending aorta distensibility and aortic arch 2D PWV were estimated along with 4D PWV. Furthermore, applanation tonometry carotid-to-femoral PWV (cf PWV) was recorded immediately after MRI. Results: While no significant changes were found for aortic distensibility; cf PWV, 2D PWV and 4D PWV were significantly higher in CAD patients than controls (12.7 ± 2.9 vs. 9.6 ± 1.1; 11.0 ± 3.4 vs. 8.0 ± 2.05 and 17.3 ± 4.0 vs. 8.7 ± 2.5 m·s−1 respectively, p < 0.001). The receiver operating characteristic (ROC) analysis performed to assess the ability of stiffness indices to separate CAD subjects from controls revealed the highest area under the curve (AUC) for 4D PWV (0.97) with an optimal threshold of 12.9 m·s−1 (sensitivity of 88.6% and specificity of 94.4%). Conclusions: PWV estimated from 4D flow MRI showed the best diagnostic performances in identifying severe stable CAD patients from age and sex-matched controls, as compared to 2D flow MRI PWV, cf PWV and aortic distensibility

Deep Learning-based Automated Aortic Area and Distensibility Assessment: the Multi-Ethnic Study of Atherosclerosis (MESA)

International audienc

Comprehensive assessment of local and regional aortic stiffness in patients with tricuspid or bicuspid aortic valve aortopathy using magnetic resonance imaging

Background: We aimed to provide a comprehensive aortic stiffness description using magnetic resonance imaging (MRI) in patients with ascending thoracic aorta aneurysm and tricuspid (TAV-ATAA) or bicuspid (BAV) aortic valve. Methods: This case-control study included 18 TAV-ATAA and 19 BAV patients, with no aortic valve stenosis/severe regurgitation, who were 1:1 age-, gender- and central blood pressures (BP)-matched to healthy volunteers. Each underwent simultaneous aortic MRI and BP measurements. 3D anatomical MRI provided aortic diameters. Stiffness indices included: regional ascending (AA) and descending (DA) aorta pulse wave velocity (PWV) from 4D flow MRI; local AA and DA strain, distensibility and theoretical Bramwell-Hill (BH) model-based PWV, as well as regional arch PWV from 2D flow MRI. Results: Patient groups had significantly higher maximal AA diameter (median[interquartile range], TAV-ATAA: 47.5[42.0–51.3]mm, BAV: 45.0[41.0–47.0]mm) than their respective controls (29.1[26.8–31.8] and 28.1[26.0–32.0]mm, p < 0.0001), while BP were similar (p ≥ 0.25). Stiffness indices were significantly associated with age (ρ ≥ 0.33), mean BP (arch PWV: ρ = 0.25, p = 0.05; DA distensibility: ρ = −0.30, p = 0.02) or AA diameter (arch PWV: ρ = 0.28, p = 0.03; DA PWV: ρ = 0.32, p = 0.009). None of them, however, was significantly different between TAV-ATAA or BAV patients and their matched controls. Finally, while direct PWV measures were significantly correlated to BH-PWV estimates in controls (ρ ≥ 0.40), associations were non-significant in TAV-ATAA and BAV groups (p ≥ 0.18). Conclusions: The overlap of MRI-derived aortic stiffness indices between patients with TAV or BAV aortopathy and matched controls highlights another heterogeneous feature of aortopathy, and suggests the urgent need for more sensitive indices which might help better discriminate such diseases.Fil: Pascaner, Ariel Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Houriez Gombaud Saintonge, Sophia. Centre National de la Recherche Scientifique; Francia. Inserm; FranciaFil: Craiem, Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Gencer, Umit. Inserm; FranciaFil: Casciaro, Mariano Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Charpentier, Etienne. Inserm; FranciaFil: Bouaou, Kevin. Centre National de la Recherche Scientifique; Francia. Inserm; FranciaFil: De Cesare, Alain. Centre National de la Recherche Scientifique; Francia. Inserm; FranciaFil: Dietenbeck, Thomas. Centre National de la Recherche Scientifique; Francia. Inserm; FranciaFil: Chenoune, Yasmina. No especifíca;Fil: Kachenoura, Nadjia. Centre National de la Recherche Scientifique; Francia. Inserm; FranciaFil: Mousseaux, Elie. Inserm; FranciaFil: Soulat , Gilles. Inserm; FranciaFil: Bollache, Emilie. Centre National de la Recherche Scientifique; Francia. Inserm; Franci