Hemodynamics biomarkers quantification in cardiovascular imaging by 4D phase-contrast magnetic resonance

En imagerie cardiovasculaire, un biomarqueur est une information quantitative permettant d'établir une corrélation avec la présence ou le développement d'une pathologie cardiovasculaire. Ces biomarqueurs sont généralement obtenus grâce à l'imagerie de l'anatomie et du flux sanguin. Récemment, la séquence d'acquisition d'IRM de flux 4D a ouvert la voie à la mesure du flux sanguin dans un volume 3D au cours du cycle cardiaque. Or, ce type d'acquisition résulte d'un compromis entre le rapport signal sur bruit, la résolution et le temps d'acquisition. Le temps d'acquisition est limité et par conséquent les données sont bruitées et sous-résolues. Dans ce contexte, la quantification de biomarqueurs est difficile. L'objectif de cette thèse est d'améliorer la quantification de biomarqueurs et en particulier du cisaillement à la paroi. Deux stratégies ont été mises en œuvre pour atteindre cet objectif. Une première solution permettant le filtrage spatio-temporel du champ de vitesse a été proposée. Cette dernière a révélé l'importance de la paroi dans la modélisation d'un champ de vitesse. Une seconde approche, constituant la contribution majeure de cette thèse, s'est focalisée sur la conception d'un algorithme estimant le cisaillement à la paroi. L'algorithme, nommé PaLMA, s'appuie sur la modélisation locale de la paroi pour construire un modèle de vitesse autour d'un point d'intérêt. Le cisaillement est évalué à partir du modèle de la vitesse. Cet algorithme intègre une étape de régularisation a posteriori améliorant la quantification du cisaillement à la paroi. Par ailleurs, une approximation du filtre IRM est utilisée pour la première fois pour l'estimation du cisaillement. Enfin, cet algorithme a été évalué sur des données synthétiques, avec des écoulements complexes au sein de carotides, en fonction du niveau de bruit, de la résolution et de la segmentation. Il permet d'atteindre des performances supérieures à une méthode de référence dans le domaine, dans un contexte représentatif de la pratique clinique.In cardiovascular imaging, a biomarker is quantitative information correlated with an existing or growing cardiovascular pathology. Biomarkers are generally obtained by anatomy and blood flow imaging. Recently, the 4D Flow MRI sequence opened new opportunities in measuring the blood flow within a 3D volume along the cardiac cycle. However, this sequence is a compromise between signalto-noise ratio, resolution and acquisition time. Allocated time being limited, velocity measurements are noisy and low resolution. In that context, biomarkers' quantification is challenging. This thesis's purpose is to enhance biomarkers' quantification and particularly for the wall shear stress (WSS). Two strategies have been investigated to reach that objective. A first solution allowing the spatiotemporal filtering of the velocity field has been proposed. It revealed the importance of the wall for the velocity field modelization. A second approach, being the major contribution of this work, focused on the design of a WSS quantification algorithm. This algorithm, named PaLMA, is based on the local modelization of the wall to build a velocity model near a point of interest. The WSS is computed from the velocity model. This algorithm embeds an a posteriori regularization step to improve the WSS quantification. Besides, a blurring model of 4D Flow MRI is used for the first time in the WSS quantification context. Finally, this algorithm has been validated over synthetic datasets, with carotids' complex flows, concerning the signal-to-noise ratio, the resolution, and the segmentation. The performances of PaLMA are superior to a reference solution in that domain, within a clinical routine contex

Quantification de biomarqueurs hémodynamiques en imagerie cardiovasculaire par résonance magnétique de flux 4D

In cardiovascular imaging, a biomarker is quantitative information correlated with an existing or growing cardiovascular pathology. Biomarkers are generally obtained by anatomy and blood flow imaging. Recently, the 4D Flow MRI sequence opened new opportunities in measuring the blood flow within a 3D volume along the cardiac cycle. However, this sequence is a compromise between signalto-noise ratio, resolution and acquisition time. Allocated time being limited, velocity measurements are noisy and low resolution. In that context, biomarkers' quantification is challenging. This thesis's purpose is to enhance biomarkers' quantification and particularly for the wall shear stress (WSS). Two strategies have been investigated to reach that objective. A first solution allowing the spatiotemporal filtering of the velocity field has been proposed. It revealed the importance of the wall for the velocity field modelization. A second approach, being the major contribution of this work, focused on the design of a WSS quantification algorithm. This algorithm, named PaLMA, is based on the local modelization of the wall to build a velocity model near a point of interest. The WSS is computed from the velocity model. This algorithm embeds an a posteriori regularization step to improve the WSS quantification. Besides, a blurring model of 4D Flow MRI is used for the first time in the WSS quantification context. Finally, this algorithm has been validated over synthetic datasets, with carotids' complex flows, concerning the signal-to-noise ratio, the resolution, and the segmentation. The performances of PaLMA are superior to a reference solution in that domain, within a clinical routine contextEn imagerie cardiovasculaire, un biomarqueur est une information quantitative permettant d'établir une corrélation avec la présence ou le développement d'une pathologie cardiovasculaire. Ces biomarqueurs sont généralement obtenus grâce à l'imagerie de l'anatomie et du flux sanguin. Récemment, la séquence d'acquisition d'IRM de flux 4D a ouvert la voie à la mesure du flux sanguin dans un volume 3D au cours du cycle cardiaque. Or, ce type d'acquisition résulte d'un compromis entre le rapport signal sur bruit, la résolution et le temps d'acquisition. Le temps d'acquisition est limité et par conséquent les données sont bruitées et sous-résolues. Dans ce contexte, la quantification de biomarqueurs est difficile. L'objectif de cette thèse est d'améliorer la quantification de biomarqueurs et en particulier du cisaillement à la paroi. Deux stratégies ont été mises en œuvre pour atteindre cet objectif. Une première solution permettant le filtrage spatio-temporel du champ de vitesse a été proposée. Cette dernière a révélé l'importance de la paroi dans la modélisation d'un champ de vitesse. Une seconde approche, constituant la contribution majeure de cette thèse, s'est focalisée sur la conception d'un algorithme estimant le cisaillement à la paroi. L'algorithme, nommé PaLMA, s'appuie sur la modélisation locale de la paroi pour construire un modèle de vitesse autour d'un point d'intérêt. Le cisaillement est évalué à partir du modèle de la vitesse. Cet algorithme intègre une étape de régularisation a posteriori améliorant la quantification du cisaillement à la paroi. Par ailleurs, une approximation du filtre IRM est utilisée pour la première fois pour l'estimation du cisaillement. Enfin, cet algorithme a été évalué sur des données synthétiques, avec des écoulements complexes au sein de carotides, en fonction du niveau de bruit, de la résolution et de la segmentation. Il permet d'atteindre des performances supérieures à une méthode de référence dans le domaine, dans un contexte représentatif de la pratique clinique

NAVIER-STOKES-BASED REGULARIZATION FOR 4D FLOW MRI SUPER-RESOLUTION

International audience4D flow MRI is a promising tool in cardiovascular imaging. However, its lack of resolution can degrade some biomarkers' evaluation accuracy. The computational fluid dynamics (CFD) simulation is considered as the reference method to improve numerically the image resolution. However, CFD simulations are complex and time consuming, and matching their results with 4D Flow MRI data is very challenging. This paper aims to introduce a fast and efficient super-resolution (SR) approach thanks to the minimization of a L 2-penalized criterion, which combines a weighted least-squares data fidelity term and Navier-Stokes equations. The algorithm has been validated on synthetic and phantom datasets and compared to state-of-the-art solutions. Moreover, a prospective study is conducted on the segmentationfree application of the proposed algorithm

Reconstruction super-résolue du flux sanguin en IRM de flux 4D par pénalisation des équations de Navier-Stokes sans pré-segmentation

International audienceCes dernières années, l'intérêt pour l'IRM de flux 4D s'est accru pour sa capacité à imager l'anatomie du cœur et la vitesse 3D du flux sanguin au cours du cycle cardiaque. Toutefois, les contraintes de l'application clinique nécessitent une acquisition avec une résolution limitée qui engendrent des difficultés pour la quantification de biomarqueurs hémodynamiques d'intérêt. Dans ce travail, nous proposons une solution originale pour améliorer la résolution de la carte de vitesse qui s'affranchie de la connaissance a priori du domaine fluide. Ainsi, notre approche s'appuie sur la résolution d'un problème inverse par minimisation d'un critère composé de trois termes : un terme de fidélité aux données, un terme s'appuyant sur la mécanique des fluides et un terme de lissage spatial. Dans cette étude, nous présentons les résultats de validation obtenus sur un jeu de données synthétiques et une application clinique

Automatic 3D Boresight Estimation of IMU and Multi-Beam Echo Sounder Systems

International audienceIn most data acquisition and processing software, IMU (Inertial Measurement Unit)-MBES (Multi-Beam Echo Sounder) system boresight calibration is achieved by applying the classical patch test procedure for all boresight angles (roll, pitch, and yaw): A survey data subset is selected from overlapping areas; For each possible boresight angle values, source data are corrected, discrepancies between overlapping terrain models are computed and their minimum value enables the user to determine the "optimal" boresight angle. Some methods overcome this second step by using visual adjustment tools. In this process, the choice of the analysis area has a significant influence on the boresight estimation, but this choice is left to the user. In addition, these methods cannot handle 3D boresight optimization, as the roll, pitch and yaw scanning process would be too time consuming. Moreover, the patch test procedure does not provide any boresight precision estimate or other quality control parameters. The aim of this paper is to present some results from a research project between FUGRO, ENSTA Bretagne and CIDCO which aims to design new procedures and associated adjustment methods for 3D automated boresight calibration. Another important aim is to provide a boresight calibration angles statistical analysis which should be part of any calibration report

Segmentation-free Super-resolved 4D FLox MRI Reconstruction Exploiting Navier-Stokes Equations and Spatial Regularization

International audienceInterest in 4D blood flow MRI grows due to its ability to image the anatomic shape and the three velocity components within a volume along the cardiac cycle. However, some biomarkers' quantification from these data can be inaccurate due to the low resolution of the images. The reference method to improve the spatial resolution numerically is to run computational fluid dynamic (CFD) simulations in order to deduce the associated images in a higher resolution grid. However, such approaches induce complex time-consuming steps and require precise estimates of the vessel wall and the inlet velocity. In this work, an original segmentation-free superresolution (SR) solution is proposed using an inverse problem resolution approach by the minimization of a compound criterion involving three terms, a mechanical term based on Navier-Stokes equations, and a velocity smoothness promoting term, and a spatially weighted data fidelity term. The proposed solution has been validated regarding estimation error and computation time on simulated data and experimental acquisition from a phantom. Super-resolved velocity reconstruction demonstrates promising performance, even without segmentation knowledge, compared to state-of-the-art solutions

NAVIER-STOKES-BASED REGULARIZATION FOR 4D FLOW MRI SUPER-RESOLUTION

International audience4D flow MRI is a promising tool in cardiovascular imaging. However, its lack of resolution can degrade some biomarkers' evaluation accuracy. The computational fluid dynamics (CFD) simulation is considered as the reference method to improve numerically the image resolution. However, CFD simulations are complex and time consuming, and matching their results with 4D Flow MRI data is very challenging. This paper aims to introduce a fast and efficient super-resolution (SR) approach thanks to the minimization of a L 2-penalized criterion, which combines a weighted least-squares data fidelity term and Navier-Stokes equations. The algorithm has been validated on synthetic and phantom datasets and compared to state-of-the-art solutions. Moreover, a prospective study is conducted on the segmentationfree application of the proposed algorithm

Segmentation-free Super-resolved 4D FLox MRI Reconstruction Exploiting Navier-Stokes Equations and Spatial Regularization

International audienceInterest in 4D blood flow MRI grows due to its ability to image the anatomic shape and the three velocity components within a volume along the cardiac cycle. However, some biomarkers' quantification from these data can be inaccurate due to the low resolution of the images. The reference method to improve the spatial resolution numerically is to run computational fluid dynamic (CFD) simulations in order to deduce the associated images in a higher resolution grid. However, such approaches induce complex time-consuming steps and require precise estimates of the vessel wall and the inlet velocity. In this work, an original segmentation-free superresolution (SR) solution is proposed using an inverse problem resolution approach by the minimization of a compound criterion involving three terms, a mechanical term based on Navier-Stokes equations, and a velocity smoothness promoting term, and a spatially weighted data fidelity term. The proposed solution has been validated regarding estimation error and computation time on simulated data and experimental acquisition from a phantom. Super-resolved velocity reconstruction demonstrates promising performance, even without segmentation knowledge, compared to state-of-the-art solutions

Segmentation-free Super-resolved 4D FLox MRI Reconstruction Exploiting Navier-Stokes Equations and Spatial Regularization

International audienceInterest in 4D blood flow MRI grows due to its ability to image the anatomic shape and the three velocity components within a volume along the cardiac cycle. However, some biomarkers' quantification from these data can be inaccurate due to the low resolution of the images. The reference method to improve the spatial resolution numerically is to run computational fluid dynamic (CFD) simulations in order to deduce the associated images in a higher resolution grid. However, such approaches induce complex time-consuming steps and require precise estimates of the vessel wall and the inlet velocity. In this work, an original segmentation-free superresolution (SR) solution is proposed using an inverse problem resolution approach by the minimization of a compound criterion involving three terms, a mechanical term based on Navier-Stokes equations, and a velocity smoothness promoting term, and a spatially weighted data fidelity term. The proposed solution has been validated regarding estimation error and computation time on simulated data and experimental acquisition from a phantom. Super-resolved velocity reconstruction demonstrates promising performance, even without segmentation knowledge, compared to state-of-the-art solutions

NAVIER-STOKES-BASED REGULARIZATION FOR 4D FLOW MRI SUPER-RESOLUTION

International audience4D flow MRI is a promising tool in cardiovascular imaging. However, its lack of resolution can degrade some biomarkers' evaluation accuracy. The computational fluid dynamics (CFD) simulation is considered as the reference method to improve numerically the image resolution. However, CFD simulations are complex and time consuming, and matching their results with 4D Flow MRI data is very challenging. This paper aims to introduce a fast and efficient super-resolution (SR) approach thanks to the minimization of a L 2-penalized criterion, which combines a weighted least-squares data fidelity term and Navier-Stokes equations. The algorithm has been validated on synthetic and phantom datasets and compared to state-of-the-art solutions. Moreover, a prospective study is conducted on the segmentationfree application of the proposed algorithm