Method and system for patient-specific modeling of blood flow

• US 10,159,529 B2
• Filed: 07/30/2015
• Issued: 12/25/2018
• Est. Priority Date: 08/12/2010
• Status: Active Grant

• Current Assignee: Heartflow Inc.
• Original Assignee: Heartflow Inc.
• Inventors: Taylor, Charles A.
• Primary Examiner(s): Clow, Lori A.
• Application Number:
  US14/813,629
• Publication Number:
  US 20150363941A1
• Time in Patent Office: 1,244 Days
• Field of Search: None
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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

1. A computer-implemented method of generating and displaying indicia of significance of vessel stenoses in patients, the method comprising:

receiving patient-specific image data, non-invasively generated by a medical imaging modality, representing at least a stenosis in a vessel of a patient, wherein the vessel comprises one or more segments;

determining patient-specific geometric characteristics of the stenosis in the vessel, wherein the patient-specific geometric characteristics comprise one or more geometric measurements defining blood flow properties through the vessel stenosis;

using the determined patient-specific geometric characteristics to determine values of parameters of a reduced order or lumped parameter model, wherein the parameters of the reduced order or lumped parameter model are derived from a library of parameters comprising one or more resistance, capacitance, or inductance values defining blood flow properties corresponding to the blood flow properties defined by the one or more geometric measurements of the patient-specific geometric characteristics;

generating a reduced order or lumped parameter model representing at least the stenosis in the vessel of the patient, wherein the reduced order or lumped parameter model comprises the determined parameters present along the one or more segments of the vessel, to model blood flow through the vessel stenosis;

determining, non-invasively, a fractional flow reserve value by simulating blood flow through the vessel stenosis using the reduced order or lumped parameter model;

generating indicia of the significance of the stenosis as a function of the determined fractional flow reserve value; and

visually presenting the image data and one or more of the determined fractional flow reserve value and the indicia of the significance of the stenosis.

2. The method of claim 1, wherein a geometric characteristic of the stenosis includes a locally minimum cross-sectional area of a vessel.

3. The method claim 1, further comprising:

calculating one or more resistance, capacitance, or inductance values defining blood flow properties in the library of parameters of a reduced order or lumped parameter model, for all or a sub-group of patients with known patient-specific geometric characteristics of a stenosis in a vessel.

4. The method of claim 1, wherein the library of parameters include one or more resistance, capacitance, or inductance values defining blood flow properties that correlate with one or more measurements of patient-specific geometric characteristics.

5. The method of claim 1, further comprising applying a color overlay over the image data, wherein a color of the overlay indicates a severity of the stenosis.

6. The method of claim 1, further comprising:

generating a signal that indicates a recommended course of action based on the fractional flow reserve value.

7. The method of claim 1, wherein the indicia of the significance of stenosis comprises one or more of a myocardial perfusion risk index or a plaque vulnerability risk index.

8. A system, comprising:

memory, including a library of parameters of a reduced order or lumped parameter model, the memory storing instructions that, when executed, comprise;

receiving patient-specific image data, non-invasively generated by a medical imaging modality, representing at least a stenosis in a vessel of a patient, wherein the vessel comprises one or more segments;

determining patient-specific geometric characteristics of the stenosis in the vessel, wherein the patient-specific geometric characteristics comprise one or more geometric measurements defining blood flow properties through the vessel stenosis;

using the determined patient-specific geometric characteristics to determine values of parameters of a reduced order or lumped parameter model, wherein the parameters of the reduced order or lumped parameter model are derived from the library of parameters, the parameters comprising one or more resistance, capacitance, or inductance values defining blood flow properties corresponding to the blood flow properties defined by the one or more geometric measurements of the patient-specific geometric characteristics;

generating a reduced order or lumped parameter model representing at least the stenosis in the vessel of the patient, wherein the reduced order or lumped parameter model comprises the determined parameters present along the one or more segments of the vessel, to model blood flow through the vessel stenosis;

determining, non-invasively, a fractional flow reserve value by simulating blood flow through the stenosis in the vessel using the reduced order or lumped parameter model;

generating indicia of the significance of the stenosis as a function of the determined fractional flow reserve value; and

visually presenting the image data and one or more of the determined fractional flow reserve value and the indicia of the significance of the vessel stenosis.

9. The system claim 8, wherein the library of parameters of a reduced order or lumped parameter model is pre-calculated using all or a sub-group of patients with known patient-specific geometric characteristics of a stenosis in a vessel.

10. The system of claim 8, wherein the image data is displayed with a color overlay that indicates a severity of the stenosis.

11. The system of claim 8, wherein the memory stores instructions that, when executed, further comprise generating a signal which indicates a recommended course of action based on the fractional flow reserve value.

12. A non-transitory computer readable medium encoded with computer readable instructions which, when executed by a processor, cause the processor to:

receiving patient-specific image data, non-invasively generated by a medical imaging modality, representing at least a stenosis in a vessel of a patient, wherein the vessel comprises one or more segments;

determine patient-specific geometric characteristics of the stenosis in the vessel, wherein the patient-specific geometric characteristics comprise one or more geometric measurements defining blood flow properties through the vessel stenosis;

use the determined patient-specific geometric characteristics to determine values of parameters of a reduced order or lumped parameter model, wherein the parameters of the reduced order or lumped parameter model are derived from a library of parameters comprising one or more resistance, capacitance, or inductance values defining blood flow properties corresponding to the blood flow properties defined by the one or more geometric measurements of the patient-specific geometric;

generate a reduced order or lumped parameter model representing at least the stenosis in the vessel of the patient, wherein the reduced order or lumped parameter model comprises the determined parameters present along the one or more segments of the vessel, to model blood flow through the vessel stenosis;

determine, non-invasively, a fractional flow reserve value by simulating blood flow through the vessel stenosis using the reduced order or lumped parameter model;

generate indicia of the significance of the vessel stenosis based on the determined fractional flow reserve value; and

visually present the image data and one or more of the determined fractional flow reserve value and the indicia of the significance of the vessel stenosis.