METHOD AND SYSTEM FOR IMAGE PROCESSING TO DETERMINE PATIENT-SPECIFIC BLOOD FLOW CHARACTERISTICS

• US 20160133015A1
• Filed: 12/31/2015
• Published: 05/12/2016
• Est. Priority Date: 08/12/2010
• Status: Active Grant

• Current Assignee: Heartflow Inc.
• Original Assignee: Heartflow Inc.
• Inventors: Taylor, Charles A.
• Granted Patent: US 10,052,158 B2
• Time in Patent Office: Days
• US Class Current: 1/1
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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

1-184. -184. (canceled)

185. A method of image processing to determine a blood flow characteristic, comprising:

estimating an outlet flow rate of a stenosis based on a cross-sectional metric at an outlet of the stenosis, a cross-sectional metric at an inlet of the stenosis, and a flow rate at the inlet of the stenosis;

estimating a resistance of the stenosis based on aortic blood pressure at the inlet of the stenosis, the outlet flow rate, and a computational fluid dynamics simulation;

classifying an unknown fractional flow reserve (“

FFR”

) metric for a cardiac vessel with the stenosis as one of a plurality of different pre-defined classes based on extracted features and a learning model, wherein the extracted features include one or more estimated boundary conditions of the stenosis, including at least one of the estimated outlet flow rate or the estimated outlet resistance of the stenosis; and

generating a signal indicative of the classification.

186. The method of claim 185, wherein the classification includes generating a numerical value for the unknown FFR and, further comprising:

classifying the unknown FFR metric based on the numerical value.

187. The method of claim 185, wherein features include one or more of a degree of stenosis, a stenosis length, a stenosis position, a ventricular chamber size, a myocardial mass, a geometry of a coronary artery, and a center line of a coronary artery.

188. The method of claim 187, wherein the features include one or more of a subset of at least one patient-specific image corresponding to one or more intensities along the vessel, heart rate, subject age, or a result of a computational fluid dynamic simulation.

189. The method of claim 185, further comprising:

performing a subsequent computational fluid dynamics simulation based on the estimated outlet flow rate of the stenosis and the estimated resistance of the stenosis.

190. The method of claim 185, further comprising:

estimating a point estimation of FFR for the stenosis and generating a second signal indicative thereof.

191. The method of claim 185, further comprising:

determining the flow rate at the inlet of the stenosis using a library trained with patient data, including at least one of image or non-image data, and flow rate measurements of other patients determined via cardiac catheterization.

192. The method claim 191, further comprising:

determining a velocity based on the outlet flow rate.

193. The method of claim 185, further comprising:

iteratively estimating the resistance of the stenosis by;

calculating an initial resistance as a function of the aortic blood pressure at the inlet of the stenosis and the outlet flow rate;

performing the computational fluid dynamic simulation;

generating a subsequent resistance based on the initial resistance and a result of the computational fluid dynamic simulation, wherein the subsequent resistance is the estimated resistance; and

repeating the acts of performing and generating using a current resistance until stopping iteration criteria is satisfied.

194. The method of claim 189, further comprising:

determining the FFR for the stenosis based on a result of the subsequent computational fluid dynamics simulation.

195. A system for image processing to determine a blood flow characteristic, comprising:

a data analyzer configured to determine a fractional flow reserve (“

FFR”

) classification of an unknown FFR for a stenosis, the data analyzer, including;

a boundary condition estimator configured to estimate at least one boundary condition of a stenosis of a vessel, including at least one of an estimated outlet flow rate of the stenosis or an estimated outlet resistance of the stenosis, based on image data that includes a representation of the vessel and the stenosis, wherein the boundary condition estimator estimated an outlet flow rate of the stenosis as a function of a cross-sectional metric at an outlet of the stenosis, a cross-sectional metric at an inlet of the stenosis, and a flow rate at the inlet of the stenosis, and the boundary condition estimator estimates a resistance of the stenosis based on aortic blood pressure at the inlet of the stenosis and the outlet flow rate using an iterative algorithm;

a feature extractor configured to extract one or more features from at least one of segmented tissue of interest in image data representing the stenosis and corresponding vessel, the estimated boundary conditions of the stenosis, and intensity information from the image data or subject data; and

a classifier configured to classify the unknown FFR into one of a plurality of different pre-defined classes based on the extracted features, including the estimated outlet flow rate of the stenosis and the estimated outlet resistance of the stenosis extracted features and a learning model.

196. The system of claim 195, further comprising:

a parameter determiner configured to determine the flow rate at the inlet of the stenosis using a library trained with patient data, including at least one of image or non-image data, and flow rate measurements of other patients determined via cardiac catheterization.

197. The system of claim 195, wherein the boundary condition estimator estimates a velocity for the stenosis based on the flow rate.

198. The system of claim 195, the data analyzer, further comprising:

a CFD processor configured to perform a computational fluid dynamics simulation based on the at least one boundary condition; and

a FFR determiner configured to determine an FFR for the stenosis based on a result of the CFD processor.