DISPLAY OF AN ELECTROMAGNETIC SOURCEBASED ON A PATIENT-SPECIFIC MODEL

• US 20190328336A1
• Filed: 07/23/2018
• Published: 10/31/2019
• Est. Priority Date: 04/26/2018
• Status: Active Grant

• Current Assignee: Vektor Medical, Inc.
• Original Assignee: Vektor Medical, Inc.
• Inventors: Villongco, Christopher
• Granted Patent: US 11,013,471 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2034/105 Modelling of the patient, e.g. for ligaments or bones
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/25 Bioelectric electrodes therefor
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/319 Circuits for simulating ECG signals
• CPC Class Codes: A61B 5/339 Displays specially adapted therefor
• CPC Class Codes: A61B 5/341 Vectorcardiography [VCG]
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/364 Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
• CPC Class Codes: A61B 5/6805 Vests
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7425 : Displaying combinations of multiple images regardless of image source, e.g. displaying a reference anatomical image with a live image geometric transforms for image registration G06T3/14; enhancement using image fusion G06T5/50; algorithms for registration of images G06T7/30
• CPC Class Codes: A61B 5/743 : Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 5/7435 : Displaying user selection data, e.g. icons in a graphical user interface
• CPC Class Codes: A61B 5/7445 : Display arrangements, e.g. multiple display units
• CPC Class Codes: A61B 6/032 : Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/503 : for diagnosis of the heart
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/23213 : with fixed number of clusters, e.g. K-means clustering
• CPC Class Codes: G06F 18/2453 : non-linear, e.g. polynomial classifier
• CPC Class Codes: G06F 30/20 : Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 20/10 : using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/042 : Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/044 : Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 : Combinations of networks
• CPC Class Codes: G06N 3/047 : Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/048 : Activation functions
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 3/088 : Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06N 5/046 : Forward inferencing; Production systems
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06T 17/20 : Finite element generation, e.g. wire-frame surface description, tesselation
• CPC Class Codes: G06T 17/205 : Re-meshing
• CPC Class Codes: G06T 19/20 : Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
• CPC Class Codes: G06T 2210/41 : Medical
• CPC Class Codes: G06T 3/4007 : based on interpolation, e.g. bilinear interpolation image demosaicing G06T3/4015; edge-driven or edge-based scaling G06T3/403
• CPC Class Codes: G06V 10/00 : Arrangements for image or video recognition or understanding character recognition in images or video G06V30/10
• CPC Class Codes: G06V 10/764 : using classification, e.g. of video objects
• CPC Class Codes: G06V 2201/03 : Recognition of patterns in medical or anatomical images
• CPC Class Codes: G09B 23/285 : for injections, endoscopy, bronchoscopy, sigmoidscopy, insertion of contraceptive devices or enemas
• CPC Class Codes: G09B 23/30 : Anatomical models G09B23/281 - G09B23/288 take precedence
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders

Abstract

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

Claims

1. A method performed by a computing system for generating a representation of an electromagnetic source, the method comprising:

identifying modeled derived electromagnetic data that matches patient derived electromagnetic data of a patient, the modeled derived electromagnetic data derived from modeled electromagnetic output of the electromagnetic source generated using a computational model of the electromagnetic source, the modeled electromagnetic output having, for each of a plurality of time intervals, an electromagnetic value for locations of the electromagnetic source;

identifying a cycle within the modeled electromagnetic output from which the matching modeled derived electromagnetic data was derived;

for each of a plurality of display locations of the electromagnetic source, generating a display electromagnetic value for that display location based on the electromagnetic values of the modeled electromagnetic output of the identified cycle; and

generating a display representation of the electromagnetic source that includes, for each of the plurality of display locations, a visual representation of the display electromagnetic value for that display location.

2. The method of claim 1 wherein the display representation has geometry based on anatomical parameters of the electromagnetic source of the patient.

3. The method of claim 1 wherein the visual representation of a display electromagnetic value is a shading based on magnitude of the display electromagnetic value.

4. The method of claim 1 wherein the visual representation of a display electromagnetic value is a color selected based on magnitude of the display electromagnetic value.

5. The method of claim 1 wherein the visual representation of a display electromagnetic value is an intensity of a color based on magnitude of the display electromagnetic value.

6. The method of claim 1 wherein the display electromagnetic value is based on a difference between the electromagnetic value at the start of the cycle and the electromagnetic value at the end of the cycle.

7. The method of claim 1 further comprising, for each of a plurality of display intervals of the identified cycle, generating and outputting a display representation for that display interval.

8. The method of claim 7 wherein the display representations are output in sequence to illustrate activations of the electromagnetic source over time.

9. The method of claim 1 wherein when multiple instances of derived electromagnetic data match the patient derived electromagnetic data, the generating of the display electromagnetic value for a display location is based on a combination of the electromagnetic values of the modeled electromagnetic output from which the matching instances of the modeled derived electromagnetic data were derived.

10. The method of claim 9 wherein the combination is an average that is weighted based on closeness of the match.

11. The method of claim 1 wherein the electromagnetic source is a heart.

12. A method performed by a computing system for generating a representation of a heart, the method comprising:

identifying a modeled cardiogram or cardiograms that are similar to patient cardiogram or cardiograms of a patient;

for each of a plurality of display locations of the heart, generating a display electromagnetic value for that display location based on an electromagnetic value of modeled electromagnetic output of a heart from which the modeled cardiogram is derived, the modeled electromagnetic output generated using a computational model of the heart; and

generating a display representation of the heart that includes, for each of the plurality of display locations, a visual representation of the display electromagnetic value for that display location.

13. The method of claim 12 wherein the display representation has geometry based on anatomical parameters of the heart of the patient.

14. The method of claim 12 wherein the visual representation of a display electromagnetic value is an intensity of a color based on magnitude of the display electromagnetic value.

15. The method of claim 12 wherein the displayed electromagnetic value is based on a difference between the electromagnetic value at the start of a model cycle within the modeled electromagnetic output and the electromagnetic value at the end of the model cycle.

16. The method of claim 15 wherein the model cycle is selected based on similarity to a patient cycle within the cardiogram.

17. The method of claim 15 wherein the model cycle is selected based on proportion of cycles which match to a location over a particular time interval.

18. The method of claim 12 further comprising for each of a plurality of display intervals of the modeled electromagnetic output, generating and outputting a display representation for that display interval.

19. The method of claim 17 wherein the display representations are output in sequence to illustrate activations of the electromagnetic source over time.

20. A computing system for displaying a representation of electrical activation of a heart of a patient, the computing system comprising:

one or more computer-readable storage mediums storing computer-executable instructions for controlling the computing system to;

identify a modeled cardiogram that is similar to a patient cardiogram of the patient;

generate a display representation of the heart that includes, for each of a plurality of display locations of the heart, a visual representation of a display value for that display location or locations, the display values being based on modeled electromagnetic output of a heart from which the modeled cardiogram is derived, the modeled electromagnetic output generated using a computational model of the heart; and

display the display representation; and

one or more processors for executing the computer-executable instructions stored in the one or more computer-readable storage mediums.

21. The computing system of claim 20 wherein the display representation has geometry based on anatomical parameters of the heart of the patient.