When Cardiac Biophysics Meets Groupwise Statistics: Complementary Modelling Approaches for Patient-Specific Medicine

This habilitation manuscript contains research on biophysical and statistical modeling of the heart, as well as interactions between these two approaches

When Cardiac Biophysics Meets Groupwise Statistics: Complementary Modelling Approaches for Patient-Specific Medicine

This habilitation manuscript contains research on biophysical and statistical modeling of the heart, as well as interactions between these two approaches

Eikonal Model Personalisation using Invasive Data to Predict Cardiac Resynchronisation Therapy Electrophysiological Response

International audienceIn this manuscript, we personalise an Eikonal model of cardiac wave front propagation using data acquired during an invasive electro-physiological study. To this end, we use a genetic algorithm to determine the parameters that provide the best fit between simulated and recorded activation maps during sinus rhythm. We propose a way to parameterise the Eikonal simulations that take into account the Purkinje network and the septomarginal trabecula influences while keeping the computational cost low. We then re-use these parameters to predict the cardiac resynchronisation therapy electrophysiological response by adapting the simulation initialisation to the pacing locations. We experiment different divisions of the myocardium on which the propagation velocities have to be optimised. We conclude that separating both ventricles and both endocardia seems to provide a reasonable personalisation framework in terms of accuracy and predictive power

Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers

Image registration is an essential but challenging task in medical image computing, especially for echocardiography, where the anatomical structures are relatively noisy compared to other imaging modalities. Traditional (non-learning) registration approaches rely on the iterative optimization of a similarity metric which is usually costly in time complexity. In recent years, convolutional neural network (CNN) based image registration methods have shown good effectiveness. In the meantime, recent studies show that the attention-based model (e.g., Transformer) can bring superior performance in pattern recognition tasks. In contrast, whether the superior performance of the Transformer comes from the long-winded architecture or is attributed to the use of patches for dividing the inputs is unclear yet. This work introduces three patch-based frameworks for image registration using MLPs and transformers. We provide experiments on 2D-echocardiography registration to answer the former question partially and provide a benchmark solution. Our results on a large public 2D echocardiography dataset show that the patch-based MLP/Transformer model can be effectively used for unsupervised echocardiography registration. They demonstrate comparable and even better registration performance than a popular CNN registration model. In particular, patch-based models better preserve volume changes in terms of Jacobian determinants, thus generating robust registration fields with less unrealistic deformation. Our results demonstrate that patch-based learning methods, whether with attention or not, can perform high-performance unsupervised registration tasks with adequate time and space complexity. Our codes are available https://gitlab.inria.fr/epione/mlp\_transformer\_registratio

Deliverable D10.4.1

This deliverable describes the final status of Task 10.4 of Workpackage 10 of the euHeart project. The aim of this task is to develop a prototype of an endovascular simulator of cardiac radiofrequency ablation. More precisely, its purpose is to simulate the patient-specific catheter navigation and radiofre- quency ablation of ventricular tachycardia. Since deliverable 10.4.1, work on the simulator prototype has focused on the development of a user interface and the integration of two software compo- nents : endovascular simulation and electrophysiology simulation. The first component aims at modeling the deformation of catheters and guidewires inside vessels and to generate a realistic visualization of the vis- ible X-ray images. The second component is focused on the simulation of electrophysiology. We have chosen the Mitchell-Schaeffer phenomenological model to represent the evolution of action potential on the myocardium. The integration of those 2 software components is difficult because they should both run simultaneously in real-time. To this end, we have developed a multi-thread framework allowing to parallelize the computation of the catheter deformation and the cardiac electrophysiology while sharing a minimum num- ber of information. We have also developed a user interface that can display X-ray images, 3D view of the heart and simulated electro-physiology signals measured at the tip of the catheter. An example of simulation is provided starting from the endovascular navi- gation from the veina cava and finishing with the radiofrequency ablation of endocardial tissue inside the right ventricle

Deliverable D10.4.2

This deliverable describes the final status of Task 10.4 of Workpackage 10 of the euHeart project. The aim of this task is to develop a prototype of an endovascular simulator of cardiac radiofrequency ablation. More precisely, its purpose is to simulate the patient-specific catheter navigation and radiofre- quency ablation of ventricular tachycardia. Since deliverable 10.4.1, work on the simulator prototype has focused on the development of a user interface and the integration of two software compo- nents : endovascular simulation and electrophysiology simulation. The first component aims at modeling the deformation of catheters and guidewires inside vessels and to generate a realistic visualization of the vis- ible X-ray images. The second component is focused on the simulation of electrophysiology. We have chosen the Mitchell-Schaeffer phenomenological model to represent the evolution of action potential on the myocardium. The integration of those 2 software components is difficult because they should both run simultaneously in real-time. To this end, we have developed a multi-thread framework allowing to parallelize the computation of the catheter deformation and the cardiac electrophysiology while sharing a minimum num- ber of information. We have also developed a user interface that can display X-ray images, 3D view of the heart and simulated electro-physiology signals measured at the tip of the catheter. An example of simulation is provided starting from the endovascular navi- gation from the veina cava and finishing with the radiofrequency ablation of endocardial tissue inside the right ventricle

Style Data Augmentation for Robust Segmentation of Multi-Modality Cardiac MRI

International audienceWe propose a data augmentation method to improve thesegmentation accuracy of the convolutional neural network on multi-modality cardiac magnetic resonance (CMR) dataset. The strategy aims to reduce over-fitting of the network toward any specific intensity or contrast of the training images by introducing diversity in these two aspects. The style data augmentation (SDA) strategy increases the size of the training dataset by using multiple image processing functions including adaptive histogram equalisation, Laplacian transformation, Sobel edge detection, intensity inversion and histogram matching. For the segmentation task, we developed the thresholded connection layer network (TCL-Net), a minimalist rendition of the U-Net architecture, which is designed to reduce convergence and computation time. We integrate the dual U-Net strategy to increase the resolution of the 3D segmentation target. Utilising these approaches on a multi-modality dataset, with SSFP and T2 weighted images as training and LGE as validation, we achieve 90% and 96% validation Dice coefficient for endocardium and epicardium segmentations. This result can be interpreted as a proof of concept for a generalised segmentation network that is robust to the quality or modality of the input images. When testing with our mono-centric LGE image dataset, the SDA method also improves the performance of the epicardium segmentation, with an increase from 87% to 90% for the single network segmentation

Improving Understanding of Long-Term Cardiac Functional Remodelling via Cross-Sectional Analysis of Polyaffine Motion Parameters

International audienceChanges in cardiac motion dynamics occur as a direct result of alterations in structure, hemodynamics, and electrical activation. Abnormal ventricular motion compromises long-term sustainability of heart function. While motion abnormalities are reasonably well documented and have been identified for many conditions, the remodelling process that occurs as a condition progresses is not well understood. Thanks to the recent development of a method to quantify full ventricular motion (as opposed to 1D abstractions of the motion) with few comparable parameters, population-based statistical analysis is possible. A method for describing functional remodelling is proposed by performing statistical cross-sectional analysis of spatio-temporally aligned subject-specific polyaffine motion parameters. The proposed method is applied to pathological and control datasets to compare functional remodelling occurring as a process of disease as opposed to a process of ageing

Towards Hyper-Reduction of Cardiac Models using Poly-Affine Deformation

International audienceThis paper presents a method for frame-based finite element model in order to develop fast personalised cardiac electromechanical models. Its originality comes from the choice of the deformation model: it relies on a reduced number of degrees of freedom represented by affine transformations located at arbitrary control nodes over a tetrahedral mesh. This is motivated by the fact that cardiac motion can be well represented by such poly-affine transformations. The shape functions use then a geodesic distance over arbitrary Voronoï-like regions containing the control nodes. The high order integration of elastic energy density over the domain is performed at arbitrary integration points. This integration , which is associated to affine degrees of freedom, allows a lower computational cost while preserving a good accuracy for simple geometry. The method is validated on a cube under simple compression and preliminary results on simplified cardiac geometries are presented, reducing by a factor 100 the number of degrees of freedom