J
P
Last name: JAÏS
First name: Pierre
International audience
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...
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...
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...
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...
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...
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...
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...
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,...
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...
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...
ObjectiveThis study sought to determine whether 3-dimensionally-reconstructed computed tomography (CT) channels correlate with electrophysiological isthmuses during ventricular tachycardia (VT).MethodsWe retrospectively studied 9 postinfarction patients (aged 57 ± 15 years, 1 female) with 10 complete VT activation maps (cycle length 429 ± 77ms)...