CALCULATING A FRACTIONAL FLOW RESERVE

• US 20150342551A1
• Filed: 01/15/2014
• Published: 12/03/2015
• Est. Priority Date: 10/24/2012
• Status: Active Grant

• Current Assignee: Cathworks Ltd.
• Original Assignee: Cathworks Ltd.
• Inventors: LAVI, Ifat, KORNOWSKI, Ran, AVRAHAMI, Idit, BENISHTI, Nessi, LAVI, Guy
• Granted Patent: US 10,595,807 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2576/023 for the heart
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/7289 Retrospective gating, i.e. associating measured signals or images with a physiological event after the actual measurement or image acquisition, e.g. by simultaneously recording an additional physiological signal during the measurement or image acquisition
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/481 involving the use of contrast agents
• 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 6/5235 combining images from the same or different ionising radiation imaging techniques, e.g. PET and CT
• CPC Class Codes: A61B 6/541 involving acquisition triggered by 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: G06T 2207/10116 X-ray image
• CPC Class Codes: G06T 2207/20072 Graph-based image processing
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 2207/30104 Vascular flow; Blood flow; Perfusion
• CPC Class Codes: G06T 2207/30172 Centreline of tubular or elongated structure
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06T 7/55 : from multiple images
• 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

A method for vascular assessment is disclosed. The method, in some embodiments, comprises receiving a plurality of 2-D angiographic images of a portion of a vasculature of a subject, and processing the images to produce a stenotic model over the vasculature, the stenotic model having measurements of the vasculature at one or more locations along vessels of the vasculature. The method, in some embodiments, further comprises obtaining a flow characteristic of the stenotic model, and calculating an index indicative of vascular function, based, at least in part, on the flow characteristic in the stenotic model.

Claims

1. A method for assessing a vascular function of a cardiac vasculature having at least one stenotic segment, the method comprising:

receiving by image processing circuitry a first vascular model modeling the cardiac vasculature;

determining at least one characteristic of flow through the stenotic segment in the first vascular model;

generating a second vascular model from the first vascular model, with at least one modification to the second vascular model which produces a difference in the characteristic of flow for the second vascular model;

further determining the characteristic of flow for the second vascular model corresponding to the characteristic of flow for the first vascular model; and

calculating a flow index quantifying the vascular function of the cardiac vasculature modeled by the first vascular model, wherein the calculating is based on comparing the characteristics of flow in said first and said second vascular models.

3. The method of claim 1, wherein said first vascular model is calculated based on a plurality of 2-D angiographic images.

4. The method of claim 3, wherein said angiographic images are of sufficient resolution to allow determination of vascular width within 10%, for a vessel segment following an at least third branch point from a main human coronary artery.

6. The method according to claim 1, wherein said flow index is calculated based on a ratio of corresponding flow characteristics of said first and second vascular models.

9. The method according to claim 1, wherein said at least one characteristic of flow comprises a flow rate.

10. The method according to claim 9, wherein said flow index comprises an index representing a Fractional Flow Reserve index comprising a ratio of the maximal flow through the stenotic segment, to the maximal flow through the stenotic segment with the stenosis removed.

11. The method according to claim 9, wherein said flow index is used in determining a recommendation for revascularization.

13. The method according to claim 1, wherein said first and said second vascular models comprise connected branches of vascular segment data, each said branch being associated with a corresponding vascular resistance to flow.

14. The method according to claim 13, wherein said first vascular model does not include a radially detailed 3-D description of the vascular wall.

15. The method according to claim 1, wherein said second vascular model comprises a relatively enlarged-diameter vessel replacing the at least one stenotic segment in said first vascular model.

16. The method according to claim 1, wherein said second vascular model comprises a normalized vessel obtained by normalizing the stenotic segment based on properties of a neighboring astenotic segment.

17. The method according to claim 1, wherein said at least one characteristic of flow is calculated based on properties of a plurality of vascular segments in flowing connection with said at least one stenotic segment.

19. The method according to claim 1, further comprising:

identifying in said first vascular model the stenosed vessel and a crown of vascular branches downstream of the stenosed vessel, and calculating a resistance to fluid flow in said crown;

wherein

said flow index is calculated based on a volume of said crown, and on a contribution of the stenosed vessel to said resistance to fluid flow.

24. The method according to claim 1, wherein each vascular model corresponds to a portion of the vasculature which extends at least three bifurcations of the vasculature distally beyond the stenotic segment.

25. The method according to claim 3, wherein the first vascular model comprises paths along vascular segments of the cardiac vasculature, each of said paths being mapped along its extent to positions in said plurality of 2-D images.

26. The method of claim 1, further comprising acquiring images of said cardiac vasculature, and constructing the first vascular model therefrom;

and

wherein each vascular model corresponds to a portion of the cardiac vasculature which extends distally as far as resolution of said images allows determination of vascular width within 10% of the correct value.

29. A computer software product, comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a computer, cause the computer to execute the method according to claim 1.

35. The method of claim 1, wherein the modification of the second vascular model from the first vascular model comprises neglecting the limitation of flow by the stenotic segment.

2. (canceled)

5. (canceled)

7-8. -8. (canceled)

12. (canceled)

18. (canceled)

20-23. -23. (canceled)

27-28. -28. (canceled)

30. A system for assessing a vascular function of a cardiac vasculature having a stenotic segment, the system comprising a computer configured to:

receive a plurality of 2-D images of the cardiac vasculature;

convert the plurality of 2-D images to a first vascular model modeling the cardiac vasculature;

determine at least one characteristic of flow in the first vascular model through the stenotic segment;

generate a second vascular model from the first vascular model which produces at least one modification which alters said at least one characteristic of flow;

further determine the characteristic of flow for the second vascular model corresponding to the characteristic of flow for the first vascular model; and

calculate a flow index quantifying the vascular function of the cardiac vasculature modeled by the first vascular model, wherein the computer calculates the flow index by comparing the characteristic of flow in said first and said second model.

32. The system of claim 30, wherein said computer is configured to calculate said flow index within 5 minutes of the acquisition of said 2-D images.

31. (canceled)

33-34. -34. (canceled)