IP Library › Granted Patent US 12,277,651
Granted Patent B1
US 12,277,651 · App. 18/737,950 · Granted Apr 15, 2025

3D cardiac visualization system

Inventors: Christopher J. T. Villongco (Oakland, CA); Robert Joseph Krummen (Bellevue, WA); Christian David Marton (Jersey City, NJ)
Assignee: THE VEKTOR GROUP, INC.
G06T17/20A61B5/339G06T7/0012G06T7/11G06T13/20G06T15/08G06T2200/04G06T2207/10016G06T2207/30048G06T2210/41
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,277,651
App. No.
18/737,950
Granted
Apr 15, 2025
Kind
B1
Abstract

A system is provided for generating and displaying a sequence of three-dimensional (3D) graphics that illustrate motion of the heart over a cardiac cycle. The system generates a start heart wall mesh and an end heart wall mesh that represent a start geometry and an end geometry of a heart wall derived from a start 3D image and an end 3D image. The system then generates one or more an intermediate heart wall 3D meshes based on an intermediate geometry of the heart wall. An intermediate geometry is interpolated based on the start geometry and the end geometry factoring a start time of a start heart wall mesh, an end time of the end heart wall mesh, and an intermediate time for the intermediate heart wall mesh. The system then displays in sequence representations the heart wall 3D meshes to illustrate the motion.

Claims (46)

1. One or more computing systems comprising:

one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems to:

access a first heart wall three-dimensional (3D) mesh that represents a first geometry of a heart wall derived from a first heart wall 3D image of a heart and that is associated with a first indicator;

generate a second heart wall 3D mesh that represents a second geometry of the heart wall that is based on the first geometry, a second indicator, and a first volume associated with the first heart wall 3D mesh and a second volume associated with the second heart wall 3D mesh; and

display in sequence representations the first heart wall 3D mesh and the second 3D heart wall mesh;

wherein the second heart wall 3D mesh is generated further based on a third heart wall 3D mesh that represents a third geometry of the heart wall derived from a third 3D image of the heart and that is associated with a third indicator and

one or more processors for controlling the one or more computing systems to execute one or more of the computer-executable instructions.

2. The one or more computing systems of claim 1 wherein the first indicator is a first time with a cardiac cycle and the second indicator is a second time within the cardiac cycle, and wherein the second volume is derived from a mapping of time within a cardiac cycle to chamber volume.

3. The one or more computing systems of claim 1 wherein a plurality of second heart wall 3D meshes are generated for different second indicators and the computer-executable instructions further include instructions to display in the sequence representations of the plurality of second heart wall 3D meshes.

4. The one or more computing systems of claim 1 wherein first indicator represents a first time within a cardiac cycle, the second indicator represents a second time within the cardiac cycle, and the third indicator represents a third time within the cardiac cycle and wherein the second time is in between the first time and the third time.

5. The one or more computing systems of claim 1 wherein first indicator represents a first time within a cardiac cycle, the second indicator represents a second time within the cardiac cycle, and the third indicator represents a third time within the cardiac cycle and wherein the second time is not between the first time and the third time.

6. The one or more computing systems of claim 1 wherein the first indicator represents a first time within a cardiac cycle, the second indicator represents a second time within the cardiac cycle, and the third indicator represents a third time within the cardiac cycle and wherein the second time is in between the first time and the third time and wherein the computer-executable instructions further include instructions to generate plurality of second heart wall 3D meshes for different second times and to display in the sequence representations of the plurality of second heart wall 3D meshes.

7. The one or more computing systems of claim 6 wherein the computer-executable instructions include instructions that display an electrocardiogram of a cardiac cycle.

8. The one or more computing systems of claim 7 wherein the first 3D image and the third 3D images are collected during the cardiac cycle.

9. The one or more computing systems of claim 8 wherein the computer-executable instructions include instructions to display a cycle time indicator in association with the displayed electrocardiogram to indicate the times associated with the 3D meshes as their representations are displayed.

10. The one or more computing systems of claim 1 wherein each heart wall 3D mesh includes vertices corresponding to an inner layer and an outer layer of the heart wall.

11. The one or more computing systems of claim 10 wherein the displayed representations provide a slice view that illustrates the inner layer, a myocardial layer, and the outer layer of a heart wall.

12. One or more computing systems comprising:

one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems to:

generate a start heart wall 3D mesh representing a start geometry of a heart wall, the start heart wall 3D mesh having vertices, one or more of the vertices are each associated with a start characteristic value of a characteristic;

generate an end heart wall 3D mesh representing an end geometry of the heart wall, the end heart wall 3D mesh having vertices, one or more of the vertices are each associated with an end characteristic value;

generate intermediate heart wall 3D meshes that each represents an intermediate geometry of the heart wall, each intermediate heart wall 3D mesh having vertices, one or more of the vertices of a heart wall 3D mesh is each associated with an intermediate characteristic value; and

display 3D graphics derived from the heart wall 3D meshes wherein the 3D graphics are displayed in sequence based on a time associated with each heart wall 3D mesh;

wherein the start heart wall 3D mesh and the end heart wall 3D mesh are derived from a simulation of electrical activity of a heart, wherein the simulation is based on a source location of an arrhythmia that is associated with a simulated heart wall 3D mesh, wherein the source location is associated with corresponding vertices of the start heart wall 3D mesh, the intermediate heart wall 3D meshes, and the end heart wall 3D mesh, and wherein the source location is indicated on the displayed 3D graphics and

one or more processors for controlling the one or more computing systems to execute one or more of the computer-executable instructions.

13. One or more computing systems comprising:

one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems to:

generate a start heart wall 3D mesh representing a start geometry of a heart wall, the start heart wall 3D mesh having vertices, one or more of the vertices are each associated with a start characteristic value of a characteristic;

generate an end heart wall 3D mesh representing an end geometry of the heart wall, the end heart wall 3D mesh having vertices, one or more of the vertices are each associated with an end characteristic value;

generate intermediate heart wall 3D meshes that each represents an intermediate geometry of the heart wall, each intermediate heart wall 3D mesh having vertices, one or more of the vertices of a heart wall 3D mesh is each associated with an intermediate characteristic value; and

display 3D graphics derived from the heart wall 3D meshes wherein the 3D graphics are displayed in sequence based on a time associated with each heart wall 3D mesh;

wherein the computer-executable instructions further include instructions to receive a user indication to rotate the display of the heart wall 3D meshes around a specified axis of rotation and

one or more processors for controlling the one or more computing systems to execute one or more of the computer-executable instructions.

14. The one or more computing systems of claim 13 wherein the intermediate heart wall 3D meshes are derived from the start heart wall 3D mesh and the end heart wall 3D mesh.

15. The one or more computing systems of claim 13 wherein geometries of the heart wall 3D meshes are based on a simulation of motion of the heart wall.

16. The one or more computing systems of claim 15 wherein the simulation simulates electrical activity of a heart.

17. The one or more computing systems of claim 13 wherein the computer-executable instructions further include instructions to access a collection of simulation data derived from simulations of electrical activity of a heart, the simulated data for a simulation including simulated heart wall 3D meshes representing geometries of a heart wall during the simulation of electrical activity of a heart and including a simulated electrocardiogram derived from the simulated electrical activity and the start heart wall 3D mesh, the intermediate heart wall 3D meshes, and the end heart wall 3D mesh are simulated heart wall 3D meshes.

18. The one or more computing systems of claim 17 wherein the computer-executable instructions further include instructions to access a patient electrocardiogram and identify a simulated electrocardiogram derived from a simulation is similar to the patient electrocardiogram based on a similarity criterion and wherein the start heart wall 3D mesh, the intermediate 3D heart wall meshes, and the end heart wall 3D mesh are based on the simulated heart wall 3D meshes of the identified simulated simulation.

19. One or more computing systems comprising:

one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems to:

generate a start heart wall 3D mesh representing a start geometry of a heart wall, the start heart wall 3D mesh having vertices, one or more of the vertices are each associated with a start characteristic value of a characteristic;

generate an end heart wall 3D mesh representing an end geometry of the heart wall, the end heart wall 3D mesh having vertices, one or more of the vertices are each associated with an end characteristic value;

generate intermediate heart wall 3D meshes that each represents an intermediate geometry of the heart wall, each intermediate heart wall 3D mesh having vertices, one or more of the vertices of a heart wall 3D mesh is each associated with an intermediate characteristic value; and

display 3D graphics derived from the heart wall 3D meshes wherein the 3D graphics are displayed in sequence based on a time associated with each heart wall 3D mesh;

wherein the computer-executable instructions further include instructions to display indications of the characteristic values of one or more characteristics associated with the 3D meshes, the characteristics including one or more of source location of an arrhythmia source, heart wall thickness, heart wall strain, heart wall strain rate, heart wall conduction velocity, tissue state, voltage, and electrical activation timing and

one or more processors for controlling the one or more computing systems to execute one or more of the computer-executable instructions.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 1, 2024
From: VILLONGCO, CHRISTOPHER J.T.; KRUMMEN, ROBERT JOSEPH; MARTON, CHRISTIAN DAVID
To: THE VEKTOR GROUP, INC.
Reel/Frame 068151/0274 →
References Cited (41)
US 5889524A · Sheehan · 1999 [cited by examiner]
US 6301496B1 · Reisfeld · 2001 [cited by examiner]
US 7043062B2 · Gerard · 2006 [cited by examiner]
US 10448901B2 · McVeigh et al. · 2019 [cited by applicant]
US 11250628B2 · Su · 2022 [cited by examiner]
US 11896432B2 · Villongco · 2024 [cited by examiner]
US 20030160786A1 · Johnson · 2003 [cited by examiner]
US 20080137929A1 · Chen · 2008 [cited by examiner]
US 20080317308A1 · Wu · 2008 [cited by examiner]
US 20090136103A1 · Sonka · 2009 [cited by examiner]
US 20160379372A1 · Groth · 2016 [cited by examiner]
US 20190333643A1 · Villongco · 2019 [cited by examiner]
US 20220047868A1 · Odland · 2022 [cited by examiner]
US 20220175295A1 · Montag · 2022 [cited by examiner]
US 20220370033A1 · Klingensmith · 2022 [cited by examiner]
US 20220409160A1 · Buckler · 2022 [cited by examiner]
US 20230225800A1 · Grund · 2023 [cited by examiner]
US 20240202919A1 · Shige · 2024 [cited by examiner]
US 20240215863A1 · Hirson · 2024 [cited by examiner]
WO 2021163227A1 · 2021 [cited by applicant]
WO 2023150644A1 · 2023 [cited by applicant]
Aguado-Sierra et al., “Patient-Specific Modeling of Dyssynchronous Heart Failure: A Case Study,” Prog Biophys Mol Biol., Oct. 2011, 23 pages. [cited by applicant]
Al-Issa et al., “Regional function analysis of left atrial appendage using motion estimation CT and risk of stroke in patients with atrial fibrillation,” European Heart Journal—Cardiovascular Imaging, Jul. 2016, 9 pages. [cited by applicant]
Bruns et al., “Deep learning-based whole-heart segmentation in 4D contrast-enhanced cardiac CT,” Computers in Biology and Medicine, Mar. 2022, 9 pages. [cited by applicant]
Bustamante et al., “Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning,” J. Magn. Reson. Imaging, Jan. 2023, pp. 191-203. [cited by applicant]
Chen et al., “Myocardial Regional Shortening from 4D Cardiac CT Angiography for the Detection of Left Ventricular Segmental Wall Motion Abnormality,” Radiology Cardiothoracic Imaging, Mar. 2023 March, vol. 5, No. 2-2023… [cited by applicant]
Fisher et al., “Multiphasic Cardiac Magnetic Resonance Imaging: Normal Regional Left Ventricular Wall Thickening,” AJR Am J Roentgenol, Jul. 1985, 4 pages. [cited by applicant]
Vetter et al., “Mechanoelectric Feedback in a Model of the Passively Inflated Left Ventricle,” Annals of Biomedical Engineering, vol. 29, May 2001, pp. 414-426. [cited by applicant]
Johansen et al., “The Investigation of Left Atrial Structure and Stroke Etiology: The I-LASER Study,” Journal of the American Heart Association, Jan. 2021, 14 pages. [cited by applicant]
Xiao et al., “MAE-TransRNet: An improved transformer-ConvNet architecture with masked autoencoder for cardiac MRI registration,” Front Med (Lausanne), Mar. 2023, 19 pages. [cited by applicant]
Wang et al., “Randomized Trial of Left Bundle Branch vs Biventricular Pacing for Cardiac Resynchronization Therapy,” Journal of the American College of Cardiology, Sep. 27, 2022, 12 pages. [cited by applicant]
Ronneberger et al., “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Computer Science Department and BIOSS Centre for Biological Signalling Studies, University of Freiburg, Germany, May 18, 2015, 8 pag… [cited by applicant]
Qureshi et al., “Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke,” Frontiers in Cardiovascular Medicine, Jan. 16, 2023, 14 pages. [cited by applicant]
Author Unknown, “Deep Learning Synthetic Strain: Quantitative Assessment of Regional Myocardial Wall Motion,” Radiology Society of North America, 47 pages. [cited by applicant]
Prassl et al., “Automatically Generated, Anatomically Accurate Meshes for Cardiac Electrophysiology Problems,” IEEE Trans Biomed Eng., May 2009 May, 37 pages. [cited by applicant]
Ouyang et al., “Video-based Al for beat-to-beat assessment of cardiac function,” Nature, Apr. 2020, 19 pages. [cited by applicant]
McVeigh et al., “Regional myocardial strain measurements from 4DCT in patients with normal LV function,” Journal of Cardiovascular Computed Tomography, Sep.-Oct. 2018 Sep.-Oct., pp. 372-378 (16 pages). [cited by applicant]
Liang et al., “Left Bundle Branch Pacing Versus Biventricular Pacing for Acute Cardiac Resynchronization in Patients With Heart Failure,” Circulation: Arrhythmia and Electrophysiology, Nov. 2022, pp. 751-761 (11 pages). [cited by applicant]
Lee et al., “Multiresolution Mesh Morphing,” Proceedings of SIGGRAPH 99, Aug. 1999, pp. 343-350 (8 pages). [cited by applicant]
Kong et al., “A deep-learning approach for direct whole-heart mesh reconstruction,” Medical Image Analysis, vol. 74, 2021, 35 pages. [cited by applicant]
Chen, et al., “Detection of left ventricular wall motion abnormalities from vol. rendering of 4DCT cardiac angiograms deep learning,” Frontiers in Cardiovascular Medicine, published Jul. 28, 2022, 12 pages. [cited by applicant]
Cited By (2)
US 12,361,263 US 12,651,165