Fractional flow reserve estimation

Patent metadata

Field	Value
Publication number	US-9949650-B2
Application number	US-201313842104-A
Country	US
Kind code	B2
Filing date	May 6, 2013
Priority date	Feb 29, 2012
Publication date	Apr 24, 2018
Grant date	Apr 24, 2018

Abstract

Approaches for assessing hemodynamic characteristics for an organ of interest are related. In one implementation, a fluid dynamics model may be provided with data derived from an anatomic imaging modality and blood flow information derived by ultrasound to derive the desired hemodynamic characteristics. In one such implementation, a fractional flow reserve is estimated.

First claim

The invention claimed is:

1. A microprocessor-implemented method for assessing hemodynamic information, consisting essentially of:
acquiring vasculature images depicting at least a location and topology of a narrowing of a blood vessel;
segmenting the blood vessel from a remainder of the vasculature images to extract anatomical information representing the geometry of the blood vessel, wherein the anatomical information includes a location of a narrowing of the blood vessel, branch locations of the blood vessel, and a percentage of lumen reduction at the location of the narrowing, such that a localized region based on the location of the narrowing and the branch locations is determined;
non-invasively acquiring local blood flow information in at least one or more vessels proximal to the location of the narrowing;
quantifying a pressure drop across the narrowing based on the reduced section of the blood vessel and the acquired blood flow using a computational fluid dynamics model configured to receive the geometry of the blood vessel and the blood flow information;
calculating one or more numeric values quantifying hemodynamic characteristics based on the pressure drop; and
generating and outputting for review at least the one or more calculated numeric values quantifying the hemodynamic characteristics.

2. The method of claim 1 , wherein the anatomical information further comprises one or more of a location of a vessel tree, a percentage of lumen reduction, or stenosis composition.

3. The method of claim 1 , wherein the location of the narrowing of the blood vessel comprises a stenotic lesion.

4. The method of claim 1 , wherein the local blood flow information comprises a flow boundary condition used in the computational fluid dynamics model.

5. The method of claim 1 , comprising:
employing a computed tomography (CT) system to generate a set of CT image data;
segmenting arteries from one or more CT images; and
deriving the anatomical information based at least on the segmented arteries.

6. The method of claim 5 , comprising:
employing an ultrasound system to generate a set of ultrasound data;
registering the ultrasound data to the CT image data;
identifying an ultrasound volume of interest based on the vascular images;
generating a set of velocity data for the ultrasound volume of interest; and
estimating at least the local blood flow information using the set of velocity data.

7. The method of claim 1 , comprising:
employing an interventional X-ray system to generate a set of image data;
segmenting arteries from one or more reconstructed images; and
deriving the anatomical information based at least on the segmented arteries.

8. The method of claim 7 , comprising:
employing an ultrasound system to generate a set of ultrasound data;
registering the ultrasound data to the set of image data;
identifying an ultrasound volume of interest based on the vasculature images;
generating a set of velocity data for the ultrasound volume of interest; and
estimating at least the local blood flow information using the set of velocity data.

9. One or more non-transitory computer-readable media encoding one or more processor-executable routines, wherein the one or more routines, when executed by a microprocessor, cause acts to be performed consisting essentially of:
accessing or acquiring vasculature images depicting at least a location and topology of a narrowing of a blood vessel;
segmenting the blood vessel from a remainder of the vasculature images to extract anatomical information representing the geometry of the blood vessel, wherein the anatomical information includes a location of a narrowing of the blood vessel, branch locations of the blood vessel, and a percentage of lumen reduction at the location of the narrowing, such that a localized region based on the location of the narrowing and the branch locations is determined;
accessing or acquiring non-invasively acquired local blood flow information for at least one or more vessels proximal to the narrowing;
quantifying a pressure drop across the narrowing based on the reduced section of the blood vessel and the acquired blood flow using a computational fluid dynamics model configured to receive the geometry of the blood vessel and the blood flow information;
calculating one or more numeric values quantifying hemodynamic characteristics based on the pressure drop; and
generating and outputting for review at least the one or more calculated numeric values quantifying the hemodynamic characteristics.

10. The one or more non-transitory computer-readable media of claim 9 , wherein the anatomical information further comprises one or more of a location of a vessel tree, a percentage of lumen reduction, or stenosis composition.

11. The one or more non-transitory computer-readable media of claim 9 , wherein the local blood flow information comprises a flow boundary condition used in the computational fluid dynamics model.

12. The one or more non-transitory computer-readable media of claim 9 , wherein the one or more routines, when executed by the microprocessor, cause further acts to be performed comprising:
accessing a set of computed tomography (CT) image data;
segmenting arteries from one or more CT images; and
deriving the anatomical information based at least on the segmented arteries.

13. The one or more non-transitory computer-readable media of claim 12 , wherein the one or more routines, when executed by the microprocessor, cause further acts to be performed comprising:
accessing a set of ultrasound data;
registering the ultrasound data to the one or more CT images;
identifying an ultrasound volume of interest based on the vasculature images;
generating a set of velocity data for the ultrasound volume of interest; and
estimating at least the local blood flow information using the set of velocity data.

14. The one or more non-transitory computer-readable media of claim 9 , wherein the one or more routines, when executed by the microprocessor, cause further acts to be performed comprising:
accessing a set of interventional X-ray image data;
segmenting arteries from one or more images; and
deriving the anatomical information based at least on the segmented arteries.

15. The one or more non-transitory computer-readable media of claim 14 , wherein the one or more routines, when executed by the microprocessor, cause further acts to be performed comprising:
accessing a set of ultrasound data;
registering the ultrasound data to the one or more images;
identifying an ultrasound volume of interest based on the vasculature images;
generating a set of velocity data for the ultrasound volume of interest; and
estimating at least the local blood flow information using the set of velocity data.

16. A microprocessor-based system, comprising:
a storage encoding one or more processor-executable routines, wherein the routines, when executed cause acts to be performed consisting essentially of:
acquiring vasculature images depicting at least a location and topology of a narrowing of a blood vessel;
segmenting the blood vessel from a remainder of the vasculature images to extract anatomical information representing the geometry of the blood vessel, wherein the anatomical information includes a location of a narrowing of the blood vessel, branch locations of the blood vessel, and a percentage of lumen reduction at the location of the narrowing, such that a localized region based on the location of the narrowing and the branch locations is represented;
acquiring non-invasively acquired local blood flow infor

Assignees

• Gen Electric
• Univ Michigan

Inventors

• Edic Peter Michael
• Senzig Robert Franklin
• Thomenius Kai Erik
• Gray Steven James
• Langan David Allen
• Venugopal Prem
• Fowlkes Jeffrey Brian

Classifications

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title

• G06F2111/10

  Numerical modelling · CPC title

• G06T7/0012

  Biomedical image inspection · CPC title

• A61B8/5223

  for extracting a diagnostic or physiological parameter from medical diagnostic data (for algorithms to analyse biomedical images G06T7/0012) · CPC title

• G16H50/50

  for simulation or modelling of medical disorders · CPC title