# US 10,441,361 B2 · Method and system for image processing and patient-specific modeling of blood flow

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- Number: US 10,441,361 B2
- Document ID: US10441361B2
- Status: Granted
- Kind: B2
- Application number: 15/246,766
- Filed: 2016-08-25
- Published: 2016-12-15
- Granted: 2019-10-15
- Earliest claimed: 2010-08-12
- Assignees: HeartFlow, Inc.
- Applicants: HeartFlow, Inc.
- Inventors: Charles A. Taylor
- Web page: https://patentgrep.com/patent/US10441361B2

## CPC

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- A61B 5/02028: {Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction}
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- G06G 7/60: for living beings, e.g. their nervous systems {; for problems in the medical field}
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- G06T 11/00: Two-dimensional [2D] image generation
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- A61B 2090/374: {NMR or MRI}
- A61B 2090/3762: {using computed tomography systems [CT]}
- A61B 2090/3764: {with a rotating C-arm having a cone beam emitting source}
- A61B 2576/00: Medical imaging apparatus involving image processing or analysis (A61B1/00009, A61B6/52 and A61B8/52 take precedence)
- A61B 2576/023: for the heart
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- G06T 2207/10081: Computed x-ray tomography [CT]
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- G06T 2207/20124: Active shape model [ASM]
- G06T 2207/30016: Brain
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- G06T 2207/30104: Vascular flow; Blood flow; Perfusion
- G06T 2210/41: Medical
- G06T 2211/404: Angiography
- G06V 10/467: {Encoded features or binary features, e.g. local binary patterns [LBP]}
- Y02A 90/10: Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- A61M: DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY (introducing media into or onto the bodies of animals A61D7/00; means for inserting tampons A61F13/26; devices for administering food or medicines orally A61J; containers for collecting, storing or administering blood or medical fluids A61J1/05); DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY (surgery A61B; chemical aspects of surgical articles A61L); DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR {(Electrotherapy, e.g. producing anaesthesia by the use of alternating or intermittent currents A61N1/36021)}
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06G: ANALOGUE COMPUTERS (analogue optical computing devices G06E3/00)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- Y02A: TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE

## Abstract

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

## Claims

### Claim 1

A method processing images for planning treatment for arterial stenoses, comprising:
receiving medical image data of at least a patient's coronary arteries, wherein the medical image data is non-invasively produced from a computerized tomography scanner;
identifying, using an image processor, a set of stenoses in the patient's coronary arteries from the received non-invasively produced medical image data;
generating a plurality of treatment options for the identified set of stenoses, wherein each of the plurality of treatment options corresponds to a simulated stenting configuration in which stenting of one or more of the identified stenoses is simulated; and
calculating, for each of the generated plurality of treatment options, predicted hemodynamic metrics for the identified set of stenoses resulting from the simulated stenting configuration corresponding to that treatment option.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
evaluating the generated plurality of treatment options based on the predicted blood flow characteristics calculated for each of the generated plurality of treatment options for the identified set of stenoses.

### Claim 3 (depends on 2)

The method of claim 2, further comprising:
displaying a plurality of results of calculating the predicted blood flow characteristics resulting from the simulated stenting configuration corresponding to each of the generated plurality of treatment options for the identified set of stenoses.

### Claim 4 (depends on 2)

The method of claim 2, wherein evaluating the plurality of treatment options comprises ranking the plurality of treatment options.

### Claim 5 (depends on 1)

The method of claim 1, wherein calculating, for each of the generated plurality of treatment options, predicted blood flow characteristics for the identified set of stenoses resulting from the simulated stenting configuration corresponding to that generated treatment option comprises:
calculating, for each of the generated plurality of treatment options, predicted fractional flow reserve (FFR) values for the identified set of stenoses resulting from the simulated stenting configuration corresponding to that generated treatment option.

### Claim 6 (depends on 5)

The method of claim 5, wherein calculating, for each of the generated plurality of treatment options, predicted fractional flow reserve (FFR) values for the identified set of stenoses resulting from the simulated stenting configuration corresponding to that generated treatment option comprises:
determining a pressure loss model for the one or more of the identified stenoses that are stented in the corresponding stenting configuration;
simulating blood flow and pressure in the coronary arteries of the patient with the determined pressure loss model for the one or more of the identified stenoses that are stented; and
calculating FFR values for each of the identified set of stenoses based on the simulated blood flow and pressure in the coronary arteries of the patient.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
generating a patient-specific anatomic model for blood flow through the patient's coronary arteries and the identified set of stenoses, from the medical image data of the patient;
modifying the patient-specific anatomic model for blood flow, for each of the generated plurality of treatment options; and
calculating the predicted blood flow characteristics for the identified set of stenoses resulting from the simulated stenting configuration corresponding to that generated treatment option, based on a simulation of blood flow through the modified patient-specific anatomic model for blood flow.

### Claim 8 (depends on 1)

The method of claim 1, wherein identifying a set of stenoses in a patient's coronary arteries from the received non-invasively produced medical image data comprises:
extracting a patient-specific anatomical model of the coronary arteries from the medical image data;
detecting a plurality of stenoses in the coronary arteries using the patient-specific anatomical model of the coronary arteries;
estimating a blood flow characteristic for each of the detected plurality of stenoses; and
identifying a set of hemodynamically relevant stenoses from the detected plurality of stenoses based on the blood flow characteristic estimated for each of the detected plurality of stenoses.

### Claim 9 (depends on 8)

The method of claim 8, wherein estimating a blood flow characteristic for each of the detected plurality of stenoses comprises:
simulating blood flow and pressure in the coronary arteries using a computational model of coronary circulation; and
estimating the blood flow characteristic for each of the detected plurality of stenoses based on a simulated pressure change across each of the detected plurality of stenoses.

### Claim 10 (depends on 8)

The method of claim 8, wherein estimating a blood flow characteristic for each of the detected plurality of stenoses comprises:
estimating a fractional flow reserve (FFR) value for each of the detected plurality of stenoses.

### Claim 11

A system for processing images for planning treatment for arterial stenotic lesions, the system comprising:
a data storage device storing instructions for processing images for planning treatment for arterial stenoses; and
a processor configured to execute the instructions to perform a method including the steps of:
receiving medical image data of at least a patient's coronary arteries, wherein the medical image data is non-invasively produced from a computerized tomography scanner;
identifying, using an image processor, a set of stenoses in the patient's coronary arteries from the received non-invasively produced medical image data;
generating a plurality of treatment options for the identified set of stenoses, wherein each of the plurality of treatment options corresponds to a simulated stenting configuration in which stenting of one or more of the identified stenoses is simulated; and
calculating, for each of the generated plurality of treatment options, predicted hemodynamic metrics for the identified set of stenoses resulting from the simulated stenting configuration corresponding to that generated treatment option.

### Claim 12 (depends on 11)

The system of claim 11, further comprising:
evaluating the generated plurality of treatment options based on the predicted blood flow characteristics calculated for each of the generated plurality of treatment options for the identified set of stenoses.

### Claim 13 (depends on 12)

The system of claim 12, further comprising:
displaying a plurality of results of calculating the predicted blood flow characteristics resulting from the simulated stenting configuration corresponding to each of the generated plurality of treatment options for the identified set of stenoses.

### Claim 14 (depends on 11)

The system of claim 11, wherein calculating, for each of the generated plurality of treatment options, predicted blood flow characteristics for the identified set of stenoses resulting from the simulated stenting configuration corresponding to that generated treatment option comprises:
calculating, for each of the generated plurality of treatment options, predicted fractional flow reserve (FFR) values for the identified set of stenoses resulting from the simulated stenting configuration corresponding to that generated treatment option.

### Claim 15 (depends on 14)

The system of claim 14, wherein calculating, for each of the generated plurality of treatment options, predicted fractional flow reserve (FFR) values for the identified set of stenoses resulting from the simulated stenting configuration corresponding to that generated treatment option comprises:
determining a pressure loss model for the one or more of the identified stenoses that are stented in the corresponding stenting configuration;
simulating blood flow and pressure in the coronary arteries of the patient with the determined pressure loss model for the one or more of the identified stenoses that are stented; and
calculating FFR values for each of the identified set of stenoses based on the simulated blood flow and pressure in the coronary arteries of the patient.

### Claim 16 (depends on 11)

The system of claim 11, wherein the generated plurality of treatment options comprises a respective treatment option corresponding to each possible stenting configuration of one or more of the identified set of stenoses.

### Claim 17 (depends on 11)

The system of claim 11, further comprising:
generating a patient-specific anatomic model for blood flow through the patient's coronary arteries and the identified set of stenoses, from the medical image data of the patient;
modifying the patient-specific anatomic model for blood flow, for each of the generated plurality of treatment options; and
calculating the predicted blood flow characteristics for the identified set of stenoses resulting from the simulated stenting configuration corresponding to that generated treatment option, based on a simulation of blood flow through the modified patient-specific anatomic model for blood flow.

### Claim 18 (depends on 11)

The system of claim 11, wherein identifying a set of stenoses in a patient's coronary arteries from the received non-invasively produced medical image data comprises:
extracting a patient-specific anatomical model of the coronary arteries from the medical image data;
detecting a plurality of stenoses in the coronary arteries using the patient-specific anatomical model of the coronary arteries;
estimating a blood flow characteristic for each of the detected plurality of stenoses; and
identifying a set of hemodynamically relevant stenoses from the detected plurality of stenoses based on the blood flow characteristic estimated for each of the detected plurality of stenoses.

### Claim 19 (depends on 11)

The system of claim 11, wherein evaluating the plurality of treatment options comprises ranking the plurality of treatment options.

### Claim 20

A non-transitory computer readable medium storing computer program instructions for processing images for planning treatment for arterial stenoses, the computer program instructions when executed by a processor causes the processor to perform operations comprising:
receiving medical image data of at least a patient's coronary arteries, wherein the medical image data is non-invasively produced from a computerized tomography scanner;
identifying, using an image processor, a set of stenoses in the patient's coronary arteries from the received non-invasively produced medical image data;
generating a plurality of treatment options for the identified set of stenoses, wherein each of the plurality of treatment options corresponds to a simulated stenting configuration in which stenting of one or more of the identified stenoses is simulated; and
calculating, for each of the generated plurality of treatment options, predicted hemodynamic metrics for the identified set of stenoses resulting from the simulated stenting configuration corresponding to that generated treatment option.