JAÏS , Pierre

Le domaine de l'invention concerne les méthodes de traitement d'images et de simulation pour déterminer des caractéristiques électriques particulières d'un modèle de myocarde. Plus particulièrement, le domaine de l'invention concerne les méthodes de segmentation d'une paroi du myocarde afin d'isoler et caractériser des phénomènes électriques...

Research into artificial intelligence (AI) has made tremendous progress over the past decade. In particular, the AI-powered analysis of images and signals has reached human-level performance in many applications owing to the efficiency of modern machine learning methods, in particular deep learning using convolutional neural networks. Research...

International audience

medInria is a free medical imaging software developed at Inria, which aims at providing clinicians with state-of-the-art algorithms dedicated to medical image processing and visualization. Efforts have been made to simplify the user interface, while keeping high-level algorithms. In this particular article, we will concentrate on its use in...

Non-invasive prediction of optimal targets for efficient radio-frequency ablation is a major challenge in the treatment of ventricular tachycardia.Most of the related modelling work relies on magnetic resonance imaging of the heart for patient-specific personalized electrophysiology simulations.In this study, we used high-resolution computed...

There has been a recent growing interest for cardiac computed tomography (CT) imaging in the electrophysiological community. This imaging modality indeed allows to locate and assess post-infarct scar heterogeneity, allowing to predict zones of abnormal electrical activity and even personalise EP models. To this end, most of the literature uses...

Goal: In this work, we used in silico patient-specific models constructed from 3D delayed-enhanced magnetic resonance imaging (DE-MRI) to simulate intracardiac electrograms (EGM). These included electrically abnormal electrograms as these are potential radiofrequency ablation (RFA) targets. Methods: We generated signals with distinguishable...

Aims Clinical application of patient-specific cardiac computer models requires fast and robust processing pipelines that can be seamlessly integrated into clinical workflows. We aim at building such a pipeline from computed tomography (CT) images to personalised cardiac electrophysiology (EP) model. The simulation output could be useful in the...

To analyze left atrial remodeling may reveal shape features related to post-ablation outcome in atrial fibrillation, which helps in identifying suitable candidates before ablation. In this article, we propose an application of diffeomorphometry and partial least squares regression to address this problem. We computed a template of left atrial...

BACKGROUNDElectrophysiological mapping of ventricular tachycardia (VT) is tedious and poorly reproducible. Substrate analysis on imaging cannot explicitly display VT circuits. OBJECTIVESThis study sought to introduce a computed tomography-based model personalization approach, allowing for the simulation of postinfarction VT in a clinically...

Goal: We present a model-based feature augmentation scheme to improve the performance of a learning algorithm for the detection of cardiac radio-frequency ablation (RFA) targets with respect to learning from images alone. Methods: Initially, we compute image features from delayed-enhanced MRI (DE-MRI) to describe local tissue heterogeneities...

BACKGROUND Markers of left atrial (LA) shape may improve the prediction of postablation outcomes in atrial fibrillation (AF). Correlations to LA volume and AF persistence limit their incremental value over current clinical predictors. OBJECTIVE To develop a shape score independent from AF persistence and LA volume using shape-based statistics,...

Heterogeneity of left ventricular (LV) myocardium infarction scar plays an important role as anatomical substrate in ventricular arrhythmia (VA) mechanism. LV myocardium thinning, as observed on cardiac computed tomography (CT), has been shown to correlate with LV myocardial scar and with abnormal electrical activity. In this project, we...

Radiological imaging offers effective measurement of anatomy, which is useful in disease diagnosis and assessment. Previous study has shown that the left atrial wall remodeling can provide information to predict treatment outcome in atrial fibrillation. Nevertheless, the segmentation of the left atrial structures from medical images is still...

PURPOSE To evaluate the correlation between scar as depicted by delayed enhancement (DE) MRI and the electrophysiological arrhythmogenic substrate in scar-related ventricular tachycardia (VT). METHOD AND MATERIALS 10 patients (4 ICM, 3 myocarditis, 2 ARVC, 1 non-ischemic DCM) underwent DE MRI at high spatial resolution (voxel size...

Biophysical models are increasingly used for medical applications at the organ scale. However, model predictions are rarely associated with a confidence measure although there are important sources of uncertainty in computational physiology methods. For instance, the sparsity and noise of the clinical data used to adjust the model parameters...

The tremendous advancement of cardiac imaging methods, the substantial progress in predictive modelling, along with the amount of new investigative multimodalities, challenge the current technologies in the cardiology field. Innovative, robust and multimodal tools need to be created in order to fuse imaging data (e.g., MR, CT) with mapped...

Purpose: To evaluate an automated method for the quantification of fat in the right ventricular (RV) free wall on multidetector computed tomography (CT) images and assess its diagnostic value in arrhythmogenic RV cardiomyopathy (ARVC). Materials and Methods: This study was approved by the institutional review board, and all patients gave...