# US 10,311,978 B2 · Method and system for patient specific planning of cardiac therapies on preoperative clinical data and medical images

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- Number: US 10,311,978 B2
- Document ID: US10311978B2
- Status: Granted
- Kind: B2
- Application number: 13/754,174
- Filed: 2013-01-30
- Published: 2013-08-01
- Granted: 2019-06-04
- Earliest claimed: 2012-01-30
- Assignees: Siemens Healthcare GmbH
- Applicants: Tommaso Mansi; Bogdan Georgescu; Xudong Zheng; Ali Kamen; Dorin Comaniciu
- Inventors: Tommaso Mansi; Bogdan Georgescu; Xudong Zheng; Ali Kamen; Dorin Comaniciu
- Web page: https://patentgrep.com/patent/US10311978B2

## CPC

- A61B 5/0033: {Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room}
- A61B 5/0044: {for the heart}
- A61N 1/3627: {for treating a mechanical deficiency of the heart, e.g. congestive heart failure or cardiomyopathy}
- A61N 1/36514: {controlled by a physiological quantity other than heart potential, e.g. blood pressure (controlled by two or more physical parameters A61N1/36585)}
- A61N 1/36585: {controlled by two or more physical parameters}
- G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30)
- G06T 17/00: Three-dimensional [3D] modelling for computer graphics
- G09B 23/30: Anatomical models {(G09B23/281 - G09B23/288 take precedence)}
- G16H 50/50: for simulation or modelling of medical disorders
- A61B 8/0883: {for diagnosis of the heart}
- A61B 2576/023: for the heart
- G06T 2210/41: Medical
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- A61N: ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY (measurement of bioelectric currents A61B; surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body A61B18/00; anaesthetic apparatus in general A61M; incandescent lamps H01K; infrared radiators for heating H05B)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A method and system for patient-specific planning of cardiac therapy, such as cardiac resynchronization therapy (CRT), based on preoperative clinical data and medical images, such as ECG data, magnetic resonance imaging (MRI) data, and ultrasound data, is disclosed. A patient-specific anatomical model of the left and right ventricles is generated from medical image data of a patient. A patient-specific computational heart model, which comprises cardiac electrophysiology, biomechanics and hemodynamics, is generated based on the patient-specific anatomical model of the left and right ventricles and clinical data. Simulations of cardiac therapies, such as CRT at one or more anatomical locations are performed using the patient-specific computational heart model. Changes in clinical cardiac parameters are then computed from the patient-specific model, constituting predictors of therapy outcome useful for therapy planning and optimization.

## Claims

### Claim 1

A method for patient-specific cardiac therapy planning, comprising:
generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient;
generating a patient-specific computational heart model based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data, wherein generating a patient-specific computational heart model based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data comprises:
determining patient-specific parameters of the computational heart model based on the patient-specific anatomical model of the left and right ventricles and the patient-specific clinical data by:
simulating heart function using the computational heart model, and
adjusting parameters of the computational heart model to control simulated clinical parameters resulting from the simulation of heart function using the computational heart model to match corresponding measured clinical parameters for the patient; and
simulating cardiac resynchronization therapy (CRT) at one or more anatomical locations using the patient-specific computational heart model by, wherein the computational heart model includes a cardiac electrophysiology module that simulates cardiac electrophysiology, a cardiac biomechanical module, and a cardiac boundary conditions module and the step of simulating CRT at one or more anatomical locations using the patient-specific computational heart model comprises:
simulating heart function with the patient-specific computational heart model with a stimulated current introduced at the one or more anatomical locations in the cardiac electrophysiological module of the computational heart model.

### Claim 2 (depends on 1)

The method of claim 1, wherein the step of generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient comprises:
detecting a patient-specific left ventricle model and a patient-specific right ventricle model in the medical image data; and
fusing the left ventricle model and the right ventricle model into a single bi-ventricular volumetric mesh.

### Claim 3 (depends on 2)

The method of claim 2, wherein the step of generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient further comprises:
mapping spatial information corresponding to at least one of scars, grey zones, or fibrosis onto a tetrahedral representation of bi-ventricular volumetric mesh.

### Claim 4 (depends on 2)

The method of claim 2, wherein the step of generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient further comprises:
generating a model of fiber orientation based on the bi-ventricular volumetric mesh.

### Claim 5 (depends on 4)

The method of claim 4, wherein the step of generating a model of fiber orientation based on the bi-ventricular volumetric mesh comprises:
determining a constant orientation for fibers on the epicardium and endocardium between a base plane and an apex of the heart;
determining fibers around the mitral, tricuspid, and pulmonary valves to have a circumferential orientation and fibers around the left ventricle outflow tract to have a longitudinal orientation;
performing geodesic interpolation of fiber orientations for fibers on the endocardium and epicardium between the base plane and the valves; and
calculating orientations of fibers across the myocardium using linear interpolation.

### Claim 6 (depends on 1)

The method of claim 1, wherein the electrophysiological module comprises a phenomenological model that models the change in trans-membrane potential as a summation of inward current, outward current, stimulated current, and a diffusion term.

### Claim 7 (depends on 6)

The method of claim 6, wherein the electrophysiological module comprises a first phenomenological model to model trans-membrane potential in the Purkinje fibers coupled with a second phenomenological model to model trans-membrane potential in the myocardium.

### Claim 8 (depends on 1)

The method of claim 1, wherein the step of adjusting the parameters of the computational heart model to control simulated clinical parameters resulting from the simulation of heart function using the computational heart model to match corresponding measure clinical parameters for the patient comprises:
adjusting a tissue diffusivity parameter of the electrophysiological module to align a simulated QRS with a QRS of the patient measured using ECG.

### Claim 9 (depends on 1)

The method of claim 1, wherein the cardiac biomechanical module comprises a passive component that models an elasticity of the myocardium tissue and an active component that models the active contraction of the muscle in response to an action potential.

### Claim 10 (depends on 9)

The method of claim 9, wherein the passive component comprises a co-rotational elasticity model.

### Claim 11 (depends on 9)

The method of claim 9, wherein the passive component comprises a hyper-elastic model.

### Claim 12 (depends on 9)

The method of claim 9, wherein the active component comprises a multi-scale model of myocyte contraction.

### Claim 13 (depends on 9)

The method of claim 9, wherein the step of adjusting the parameters of the computational heart model to control simulated clinical parameters resulting from the simulation of heart function using the computational heart model to match corresponding measure clinical parameters for the patient comprises:
adjusting a tissue stiffness parameter of the passive component of the cardiac biomechanical module to minimize differences between simulated cardiac motion and observed cardiac motion in the medical image data; and
adjusting an active contraction strength parameter of the active component of the cardiac biomechanical module to minimize differences between simulated cardiac motion and observed cardiac motion in the medical image data and to minimize differences between a simulated ejection fraction and a measured ejection fraction for the patient.

### Claim 14 (depends on 1)

The method of claim 1, wherein the cardiac boundary conditions module models hemodynamics parameters using lumped parameters within each of the left ventricle and the right ventricle, models arterial pressure using a lumped Windkessel model, models atrial pressure using a lumped model, and models the cardiac phases as sequential states.

### Claim 15 (depends on 14)

The method of claim 14, wherein the cardiac boundary conditions module models ventricular pressure in isovolumetric phases using correction and prediction model that corrects a current pressure estimate at a current time step such that endocardial volume is preserved and predicts a next pressure estimate at the next time step.

### Claim 16 (depends on 14)

The method of claim 14, wherein the cardiac boundary conditions module models an effect of arteries and atria on ventricular motion using a base stiffness model.

### Claim 17 (depends on 14)

The method of claim 14, wherein the cardiac boundary conditions module models effects of neighboring organs and the pericardium on ventricular motion using a pericardium constraint model.

### Claim 18 (depends on 1)

The method of claim 1, wherein the simulation of the CRT using the patient-specific heart model calculates changes in cardiac parameters after the simulated CRT, and changed cardiac parameters are used as predictors for therapy planning.

### Claim 19

An apparatus for patient-specific cardiac therapy planning, comprising:
a processor; and
a memory storing computer program instructions, which when executed by the processor cause the process to perform operations comprising:
generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient;
generating a patient-specific computational heart model based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data, wherein generating a patient-specific computational heart model based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data comprises:
determining patient-specific parameters of the computational heart model based on the patient-specific anatomical model of the left and right ventricles and the patient-specific clinical data by:
simulating heart function using the computational heart model, and
adjusting parameters of the computational heart model to control simulated clinical parameters resulting from the simulation of heart function using the computational heart model to match corresponding measured clinical parameters for the patient; and
simulating CRT at one or more anatomical locations using the patient-specific computational heart model, wherein the computational heart model includes a cardiac electrophysiology module that simulates cardiac electrophysiology, a cardiac biomechanical module, and a cardiac boundary conditions module and simulating CRT at one or more anatomical locations using the patient-specific computational heart model comprises:
simulating heart function with the patient-specific computational heart model with a stimulated current introduced at the one or more anatomical locations in the cardiac electrophysiological module of the computational heart model.

### Claim 20 (depends on 19)

The apparatus of claim 19, wherein generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient comprises:
detecting a patient-specific left ventricle model and a patient-specific right ventricle model in the medical image data; and
fusing the left ventricle model and the right ventricle model into a single bi-ventricular volumetric mesh.

### Claim 21 (depends on 20)

The apparatus of claim 20, wherein generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient further comprises:
generating a model of fiber orientation based on the bi-ventricular volumetric mesh.

### Claim 22

A non-transitory computer readable medium storing computer program instructions for patient-specific cardiac therapy planning, the computer program instructions when executed by a processor causing the processor to perform operations comprising:
generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient;
generating a patient-specific computational heart model based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data, wherein generating a patient-specific computational heart model based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data comprises:
determining patient-specific parameters of the computational heart model based on the patient-specific anatomical model of the left and right ventricles and the patient-specific clinical data by:
simulating heart function using the computational heart model, and
adjusting parameters of the computational heart model to control simulated clinical parameters resulting from the simulation of heart function using the computational heart model to match corresponding measured clinical parameters for the patient; and
simulating cardiac resynchronization therapy (CRT) at one or more anatomical locations using the patient-specific computational heart model, wherein the computational heart model includes a cardiac electrophysiology module that simulates cardiac electrophysiology, a cardiac biomechanical module, and a cardiac boundary conditions module and the simulating CRT at one or more anatomical locations using the patient-specific computational heart model comprises:
simulating heart function with the patient-specific computational heart model with a stimulated current introduced at the one or more anatomical locations in the cardiac electrophysiological module of the computational heart model.

### Claim 23 (depends on 22)

The non-transitory computer readable medium of claim 22, wherein the operation of generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient comprises:
detecting a patient-specific left ventricle model and a patient-specific right ventricle model in the medical image data; and
fusing the left ventricle model and the right ventricle model into a single bi-ventricular volumetric mesh.

### Claim 24 (depends on 23)

The non-transitory computer readable medium of claim 23, wherein the operation of generating a patient-specific anatomical model of left and right ventricles from medical image data of a patient further comprises:
generating a model of fiber orientation based on the bi-ventricular volumetric mesh.