Fractional flow reserve estimation

• US 10,034,614 B2
• Filed: 02/29/2012
• Issued: 07/31/2018
• Est. Priority Date: 02/29/2012
• Status: Active Grant

• Current Assignee: General Electric Company, Board of Regents of the University of Michigan (University of Michigan)
• Original Assignee: General Electric Company
• Inventors: Edic, Peter Michael, Senzig, Robert Franklin, Thomenius, Kai Erik, Gray, Steven James, Langan, David Allen, Venugopal, Prem, Fowlkes, Jeffrey Brian
• Primary Examiner(s): LAURITZEN, AMANDA L
• Application Number:
  US13/408,886
• Publication Number:
  US 20130226003A1
• Time in Patent Office: 2,344 Days
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/4078 Fan-beams
• CPC Class Codes: A61B 6/4085 Cone-beams
• CPC Class Codes: A61B 6/4441 the rigid structure being a C-arm or U-arm
• CPC Class Codes: A61B 6/481 involving the use of contrast agents
• CPC Class Codes: A61B 6/503 for diagnosis of the heart
• CPC Class Codes: A61B 6/504 for diagnosis of blood vessels, e.g. by angiography
• CPC Class Codes: A61B 6/507 for determination of haemodynamic parameters, e.g. perfusion CT
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: A61B 6/5247 combining images from an ionising-radiation diagnostic technique and a non-ionising radiation diagnostic technique, e.g. X-ray and ultrasound
• CPC Class Codes: A61B 6/5288 involving retrospective matching to a physiological signal
• CPC Class Codes: A61B 8/06 Measuring blood flow measuring volume flow in general G01F, e.g. G01F1/66, G01F1/72; measuring speed of fluids in general G01P5/00
• CPC Class Codes: A61B 8/0883 for diagnosis of the heart
• CPC Class Codes: A61B 8/0891 for diagnosis of blood vessels
• CPC Class Codes: A61B 8/488 involving Doppler signals
• CPC Class Codes: A61B 8/5223 for extracting a diagnostic or physiological parameter from medical diagnostic data for algorithms to analyse biomedical images G06T7/0012
• CPC Class Codes: A61B 8/5261 combining images from different diagnostic modalities, e.g. ultrasound and X-ray
• CPC Class Codes: A61B 8/5284 involving retrospective matching to a physiological signal
• CPC Class Codes: G06F 2111/10 : Numerical modelling
• CPC Class Codes: G06F 30/20 : Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06T 7/30 : Determination of transform parameters for the alignment of images, i.e. image registration
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders

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.

Claims

1. A microprocessor-implemented method for assessing hemodynamic information, consisting essentially of:

acquiring volumetric coronary 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 volumetric coronary vasculature images to extract coronary-specific anatomical information representing the geometry of the blood vessel, wherein the coronary-specific 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 a numeric value quantifying a myocardial fractional flow reserve based on the pressure drop; and

generating and outputting for review at least the calculated numeric value quantifying the myocardial fractional flow reserve.

2. The method of claim 1, wherein the coronary-specific 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;

determining a cardiac phase of interest using the set of CT image data;

segmenting the coronary arteries from one or more CT images at the cardiac phase of interest; and

deriving the coronary-specific anatomical information based at least on the segmented coronary arteries.

6. The method of claim 5, comprising:

employing an ultrasound system to generate a set of ultrasound data;

registering the ultrasound data based on the cardiac phase of interest;

identifying an ultrasound volume of interest based on the volumetric coronary 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;

determining a cardiac phase of interest using the set of image data;

segmenting the coronary arteries from one or more reconstructed images at the cardiac phase of interest; and

deriving the coronary-specific anatomical information based at least on the segmented coronary arteries.

8. The method of claim 7, comprising:

employing an ultrasound system to generate a set of ultrasound data;

registering the ultrasound data based on the cardiac phase of interest;

identifying an ultrasound volume of interest based on the volumetric coronary 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.

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 volumetric coronary 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 volumetric coronary vasculature images to extract coronary-specific anatomical information representing the geometry of the blood vessel, wherein the coronary-specific 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 a numeric value quantifying a myocardial fractional flow reserve based on the pressure drop; and

generating and outputting for review at least the calculated numeric value quantifying the myocardial fractional flow reserve.

10. The one or more non-transitory computer-readable media of claim 9, wherein the coronary-specific 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 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;

determining a cardiac phase of interest using the CT image data;

segmenting the coronary arteries from one or more CT images at the cardiac phase of interest; and

deriving the coronary-specific anatomical information based at least on the segmented coronary 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 based on the cardiac phase of interest;

identifying an ultrasound volume of interest based on the volumetric coronary vasculature images;

generating a set of velocity data for the ultrasound volume of interest; and

estimating at least the 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 image data;

determining a cardiac phase of interest using the image data;

segmenting the coronary arteries from one or more images at the cardiac phase of interest; and

deriving the coronary-specific anatomical information based at least on the segmented coronary 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 based on the cardiac phase of interest;

identifying an ultrasound volume of interest based on the volumetric coronary vasculature images;

generating a set of velocity data for the ultrasound volume of interest; and

estimating at least the 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 comprising;

acquiring volumetric coronary 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 volumetric coronary vasculature images to extract coronary-specific anatomical information representing the geometry of the blood vessel, wherein the coronary-specific 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 information comprising at least flow information corresponding to 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 a numeric value quantifying a myocardial fractional flow reserve based on the pressure drop; and

generating and outputting for review at least the calculated numeric value quantifying the myocardial fractional flow reserve;

a memory configured to encode the one or more processor-executable routines prior to execution; and

one or more microprocessors configured to access and execute the one or more routines when encoded by the memory.

17. The microprocessor-based system of claim 16, wherein the coronary-specific anatomical information further comprises one or more of a location of a vessel tree, a percentage of lumen reduction, or stenosis composition.

18. The microprocessor-based system of claim 16, wherein the blood flow information comprises a flow boundary condition used in the computational fluid dynamics model.

19. The microprocessor-based system of claim 16, wherein the location of the narrowing of the blood vessel comprises a stenotic lesion.

20. The microprocessor-based system of claim 16, wherein the routines, when executed by the one or more microprocessors, cause further acts to be performed comprising:

accessing a set of computed tomography (CT) image data;

determining a cardiac phase of interest using the CT image data;

segmenting the coronary arteries from one or more CT images at the cardiac phase of interest; and

deriving the coronary-specific anatomical information based at least on the segmented coronary arteries.

21. The microprocessor-based system of claim 20, wherein the routines, when executed by the one or more microprocessors, cause further acts to be performed comprising:

accessing a set of ultrasound data;

registering the ultrasound data based on the cardiac phase of interest;

identifying an ultrasound volume of interest based on the volumetric coronary vasculature images;

generating a set of velocity data for the ultrasound volume of interest; and

estimating at least the blood flow information using the set of velocity data.

22. The microprocessor-based system of claim 16, wherein the routines, when executed by the one or more microprocessors, cause further acts to be performed comprising:

accessing a set of interventional X-ray image data;

determining a cardiac phase of interest using the image data;

segmenting the coronary arteries from one or more images at the cardiac phase of interest; and

deriving the coronary-specific anatomical information based at least on the segmented coronary arteries.

23. The microprocessor-based system of claim 22, wherein the routines, when executed by the one or more microprocessors, cause further acts to be performed comprising:

accessing a set of ultrasound data;

registering the ultrasound data based on the cardiac phase of interest;

identifying an ultrasound volume of interest based on the volumetric coronary vasculature images;

generating a set of velocity data for the ultrasound volume of interest; and

estimating at least the blood flow information using the set of velocity data.