The Johns Hopkins University

Natalia A Trayanova

Kathleen McDowell

Patrick M Boyle

Hermenegild Arevalo

Sohail Zahid

Henry R Halperin

Rheeda Ali

Jason Constantino

Lukas Rantner

Fijoy Vadakkumpadan

Xiaozhong Chen

Adityo Prakosa

Julie Shade

Mark Cartoski

Amir Abdelwahab

John Sapp

Jonathan Chrispin

Katherine Wu

Shijie A Zhou

Intraprocedural techniques for identifying a location of an origin of an idiopathic ventricular arrhythmia in a patient are presented. The techniques include acquiring an at least partial electroanatomical geometry; acquiring an electrocardiogram segment of the idiopathic ventricular arrhythmia; calculating at least one integral of the electrocardiogram segment; acquiring a plurality of pacing site sets of coordinates; acquiring a plurality of pacing electrocardiogram segments of the patient; calculating at least one pacing integral; relating each pacing site set of coordinates to at least one corresponding pacing integral, such that a plurality of coefficients are obtained; determining an estimated set of coordinates of the origin of the idiopathic ventricular arrhythmia based on at least one integral of the electrocardiogram segment and on the plurality of coefficients; projecting the estimated set of coordinates of the origin on to the at least partial electroanatomical geometry to obtain a map; and providing the map.

Intraprocedural automated system for localizing idiopathic ventricular arrhythmia origins

Methods, system, and media for identifying one or more ablation locations in an atrial tissue region in an atrial fibrillation (AF) patient with atrial fibrosis are disclosed. Three-dimensional imaging data representing the atria of the patient may be received. A patient-specific model of the atria may be generated from the three-dimensional imaging data. Simulation of the AF on the patient-specific model may be conducted to identify AF-perpetrating regions. One or more ablation locations in the atria may be identified from the AF-perpetrating regions.

Personalized computational modeling of atrial fibrosis to guide catheter ablation of atrial fibrillation

A device may receive images of a patient, and may perform segmentation of surfaces on the images to create a 3D model. The device may identify normal tissue regions and atrial fibrosis (AF) regions in the 3D model, and may divide the 3D model into the normal tissue regions and the AF regions. The device may assign first cell and tissue properties to the normal tissue regions, and may assign second cell and tissue properties to the AF regions. The device may perform simulations on the normal tissue regions and the AD regions, based on the first and second cell and tissue properties, to generate simulation results, and may extract first features from the simulation results. The device may extract second features from the images, and may process the first and second features, with a model, to select a feature that is predictive of atrial fibrillation recurrence.

Predicting atrial fibrillation recurrence after pulmonary vein isolation using simulations of patient-specific magnetic resonance imaging models and machine learning

A method of planning a patient-specific cardiac procedure according to an embodiment of the current invention includes receiving three-dimensional imaging data of a patient's heart, simulating at least one of electrophysiological or electromechanical activity of at least a portion of the patient's heart using the three-dimensional imaging data, and planning the patient-specific cardiac procedure based on the simulating. The cardiac procedure is for providing a preselected alteration of at least one of electrophysiological or electromechanical behavior of the patient's heart.

System and method for planning a patient-specific cardiac procedure

Personalized computation modeling of atrial fibrosis to guide catheter ablation of atrial fibrillation

An embodiment in accordance with the present invention provides a non-invasive solution to risk stratify the risk of in arrhythmia in patients with TOF. Currently, no reliable method for non-invasive risk stratification exists. In the realm of congenital heart disease, cardiac MRI is now used routinely for patients with Tetralogy of Fallot (TOF), the most common form of cyanotic congenital heart disease. An innovative platform for using clinical MRI data to create 3D electromechanical models of the heart enables predictions of whether or not patients with ischemic heart disease have the substrate for arrhythmia and what their relative risk for such an event is. An embodiment of the current invention provides a non-invasive solution to risk stratify the risk of arrhythmia in patients with TOF. Currently, no reliable method for non-invasive risk stratification exists.

Risk stratification for ventricular arrhythmia in patients with repaired tetralogy of Fallot (TOF) via image-based computational simulations

A computer-implemented method for non-invasively identifying ablation locations in atrial tissue, can include: receiving three-dimensional imaging data representing atrial tissue of a left atrial flutter (LAFL) subject; generating a subject-specific model of the at least one of the atrial tissue from the three-dimensional imaging data; estimating tissue fiber orientations in the atrial tissue; assigning the estimated tissue fiber orientations to the subject-specific model of the atrial tissue; conducting simulations of LAFL using the subject-specific model to identify regions of slow conduction of a propagating wave within an atrial tissue region of the atrial tissue; a critical isthmus of a rotational wavefront within the atrial tissue region; or a region based on a minimum cut in a flow network; and identifying at least one ablation location in the atrial tissue region based on the identified regions of slow conduction, the critical isthmus, or the minimum cut.

Systems and methods for simulation prediction of targets for catheter ablation of left atrial flutter in patients with atrial structural remodeling

A method of determining a likelihood of an occurrence of a cardiac arrhythmia in a patient includes receiving three-dimensional imaging data of said patient's heart, constructing a whole-heart model for simulating at least one of electrophysiological activity or electromechanical activity of the patient's heart using the three-dimensional imaging data, simulating a response of the patient's heart to each of a plurality of stimulations to a corresponding plurality of different locations within the patient's heart using the whole-heart model, classifying each simulation outcome for each stimulation as one of a normal heart rhythm or a cardiac arrhythmia, calculating a likelihood index based on results of the classifying, and determining the likelihood of the occurrence of the cardiac arrhythmia in the patient based on the likelihood index. Software and data processing systems that implement the above methods are also provided.

System and method for personalized cardiac arrhythmia risk assessment by simulating arrhythmia inducibility

According to some embodiments of the invention, a method for providing an atrial fibrillation (AF) ablation treatment plan includes receiving imaging data for at least a portion of an atrial region of a subject's heart, and processing the imaging data to characterize tissue as one of fibrotic tissue or non-fibrotic tissue. The method further includes calculating a metric of spatial distribution of at least a portion of the tissue characterized as fibrotic tissue from the processing the imaging data, identifying a cardiac tissue ablation target based on the metric, and providing an AF treatment plan that includes the cardiac tissue ablation target as at least a portion of the AF treatment plan.

Systems and methods for atrial fibrillation treatment and risk assessment

The present invention includes a method for determining optimal placement sites for internal defibrillators in pediatric and congenital heart defect patients. The method is executed by creating a personalized active heart-torso model. The model is created using imaging scans (e.g., low resolution clinical scans) and advanced image processing techniques. The image processing results in a heart-torso mesh model. The ventricular portion of the mesh incorporates cell membrane dynamics. The combined torso-active ventricular defibrillation model can be used for patient specific modeling of the defibrillation process and optimal defibrillator placement can be determined. This method could also be used to decrease the energy needed for a defibrillation shock, because of the optimized defibrillator placement.

Method for computationally predicting optimal placement sites for internal defibrillators in pediatric and congenital heart defect patients

A system, computer-readable medium and method can include receiving three-dimensional imaging data of a subject's heart, the subject having an ICD, wherein the ICD causes an imaging artifact in the three-dimensional imaging data that includes regions that are free of the artifact and regions that are affected by the artifact; segmenting the regions that are free of the artifact into a plurality of normal tissue regions and remodeled tissue regions for the subject; extrapolating from the regions that are free of the artifact to provide extrapolated three-dimensional imaging data corresponding to the regions that are affected by the artifact; and simulating at least one of electrophysiological or electromechanical activity of the subject's heart using the segmented and extrapolated three-dimensional imaging data, the simulating including providing a preselected alteration of electrophysiological or electromechanical behavior of the subject's heart for a target of said subject-specific cardiac ablation procedure.

Systems and methods for patient-specific modeling of the heart for prediction of targets for catheter ablation of ventricular tachycardia in patients with implantable cardioverter defibrillators

The present invention is directed to a method for determining onset of ventricular arrhythmias using bounded-input bounded-output stability of QT interval (QTI) dynamics. The method of the present invention includes two parts. A first part of the method determines the dependence of each QTI on several prior QTIs and RR intervals (RRI). This determination is represented as an autoregressive model with exogenous input (ARX). A second part of the method determines the BIBO stability of the ARX model in the z-domain. The metrics associated with the first and second parts of the method are then used to predict onset of arrhythmia.

Methodology for assessing the bounded-input bounded-output instability in QT interval dynamics: application to clinical ECG with ventricular tachycardia

Growing community of inventors

Baltimore, MD, United States of America

Natalia A Trayanova