FRACTIONAL FLOW RESERVE ESTIMATION

• US 20130226003A1
• Filed: 02/29/2012
• Published: 08/29/2013
• 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, University of Michigan
• Inventors: Edic, Peter Michael, Senzig, Robert Franklin, Thomenius, Kai Erik, Langan, David Allen, Venugopal, Prem, Fowlkes, Jeffrey Brian, Gray, Steven James
• Granted Patent: US 10,034,614 B2
• Time in Patent Office: Days
• US Class Current: 600/454
• 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 method for assessing cardiac hemodynamic information, comprising:

generating or acquiring anatomic information comprising at least a location and topology of a narrowing of a blood vessel;

generating or acquiring blood flow information comprising at least flow information in one or more vessels proximal to the location of the narrowing; and

estimating a myocardial fractional flow reserve based at least on the anatomic information and the blood flow information.

2. 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 anatomic information based at least on the segmented coronary arteries.

3. The method of claim 2, 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 anatomical information;

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

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

4. 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 anatomic information based at least on the segmented coronary arteries.

5. The method of claim 4, 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 anatomical information;

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

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

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

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

8. The method of claim 1, wherein estimating the myocardial fractional flow reserve comprises providing at least the anatomic information and the flow information to a computational fluid dynamics model.

9. The method of claim 8, wherein the flow information comprises a flow boundary condition used in the computational fluid dynamics model.

10. 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 processor, cause acts to be performed comprising:

accessing anatomic information comprising at least a location and topology of a narrowing of a blood vessel;

accessing or acquiring blood flow information comprising at least flow information corresponding to one or more vessels proximal to the narrowing; and

estimating a myocardial fractional flow reserve based at least on the anatomic information and the blood flow information.

11. The one or more non-transitory computer-readable media of claim 10, wherein the one or more routines, when executed by the processor, 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 anatomic information based at least on the segmented coronary arteries.

12. The one or more non-transitory computer-readable media of claim 11, wherein the one or more routines, when executed by the processor, 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 anatomical information;

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

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

13. The one or more non-transitory computer-readable media of claim 10, wherein the one or more routines, when executed by the processor, 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 anatomic information based at least on the segmented coronary arteries.

14. The one or more non-transitory computer-readable media of claim 13, wherein the one or more routines, when executed by the processor, 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 anatomical information;

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

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

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

16. The one or more non-transitory computer-readable media of claim 10, wherein estimating the myocardial fractional flow reserve comprises providing at least the anatomic information and the flow information to a computational fluid dynamics model.

17. The one or more non-transitory computer-readable media of claim 16, wherein the flow information comprises a flow boundary condition used in the computational fluid dynamics model.

18. A processor-based system, comprising:

a storage encoding one or more processor-executable routines, wherein the routines, when executed cause acts to be performed comprising;

generating or acquiring anatomic information comprising at least a location and topology of a narrowing of a blood vessel;

generating or acquiring blood flow information comprising at least flow information corresponding to one or more vessels proximal to the narrowing; and

estimating a myocardial fractional flow reserve based at least on the anatomic information and the blood flow information;

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

a processing component configured to access and execute the one or more routines when encoded by the memory.

19. The processor-based system of claim 18, wherein the routines, when executed by the processor, 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 anatomic information based at least on the segmented coronary arteries.

20. The processor-based system of claim 19, wherein the routines, when executed by the processor, 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 anatomical information;

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

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

21. The processor-based system of claim 18, wherein the routines, when executed by the processor, 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 anatomic information based at least on the segmented coronary arteries.

22. The processor-based system of claim 21, wherein the routines, when executed by the processor, 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 anatomical information;

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

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

23. The processor-based system of claim 18, wherein the anatomic information further comprises one or more of a location of a vessel tree, a percentage of lumen reduction, or stenosis composition.

24. The processor-based system of claim 18, wherein estimating the myocardial fractional flow reserve comprises providing at least the anatomic information and the flow information to a computational fluid dynamics model.

25. The processor-based system of claim 24, wherein the flow information comprises a flow boundary condition used in the computational fluid dynamics model.

26. The processor-based system of claim 18, wherein the location of the narrowing of the blood vessel comprises a stenotic lesion.