VASCULAR FLOW ASSESSMENT

• US 20140200867A1
• Filed: 09/29/2013
• Published: 07/17/2014
• Est. Priority Date: 01/15/2013
• Status: Active Grant

• Current Assignee: Cathworks Ltd.
• Original Assignee: Cathworks Ltd.
• Inventors: Kornowski, Ran, LAVI, Ifat, Avrahami, Idit, Benishti, Nessi, Lavi, Guy
• Granted Patent: US 9,858,387 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• 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 17/00 Three dimensional [3D] modelling, e.g. data description of 3D objects
• CPC Class Codes: G06T 2200/04 involving 3D image data
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10116 X-ray image
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• 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 7/00 : Image analysis
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G16H 30/20 : for handling medical images, e.g. DICOM, HL7 or PACS
• 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
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

A method for vascular assessment is disclosed. The method comprises receiving a plurality of 2D 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 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 vascular assessment comprising:

receiving a plurality of 2D angiographic images of a portion of a vasculature of a subject, and processing said images to produce a stenotic model over the vasculature, said stenotic model having measurements of the vasculature at one or more locations along vessels of the vasculature;

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.

2. The method according to claim 1, wherein said measurements of the vasculature are at one or more locations along a centerline of at least one branch of the vasculature.

3. The method according to claim 1, wherein said flow characteristic of said stenotic model comprises resistance to fluid flow.

4. The method according to claim 3, further comprising identifying in said first stenotic model a stenosed vessel and a crown of said stenosed vessel, and calculating said resistance to fluid flow in said crown;

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

5. The method according to claim 1, wherein said flow characteristic of said stenotic model comprises fluid flow.

6. The method according to claim 1, wherein said stenotic model is a three-dimensional vessel tree.

7. The method according to claim 6, wherein said vessel tree comprises data pertaining to location, orientation and diameter of vessels at a plurality of points within said portion of the vasculature.

8. The method according to claim 1, wherein said processing said images to produce said stenotic model comprises:

extending said stenotic model by one bifurcation;

calculating a new flow characteristic in said extended stenotic model;

updating said index responsively to said new flow characteristic and according to a predetermined criterion; and

iteratively repeating said extending, said calculating and said updating.

9. The method according to claim 1, further comprising processing said images to produce a second model over said vasculature, and obtaining a flow characteristic of said second model;

wherein said calculation of said index is based on the flow characteristic in the stenotic model and on the flow characteristic in the second model.

10. The method according to claim 9, wherein said second model is a normal model, comprising an inflated vessel replacing a stenotic vessel in the stenotic model.

11. The method according to claim 9, wherein said stenotic model is a three-dimensional vessel tree and said second model is a second three-dimensional vessel tree.

12. The method according to claim 9, wherein said flow characteristics of said models comprise fluid flow.

13. The method according to claim 9, wherein said flow characteristics of said models comprise resistance to fluid flow.

14. The method according to claim 9, wherein each of the models corresponds to a portion of the vasculature which is between two consecutive bifurcations of the vasculature and which includes a stenosis.

15. The method according to claim 9, wherein each of the models corresponds to a portion of the vasculature which includes a bifurcation of the vasculature.

16. The method according to claim 9, wherein each of the models corresponds to a portion of the vasculature which includes a stenosis and which extends at least one bifurcation of the vasculature beyond the stenosis.

17. The method according to claim 9, wherein each of the models corresponds to a portion of the vasculature which includes a stenosis and which extends at least three bifurcations of the vasculature beyond the stenosis.

18. The method according to claim 16, wherein each of the models corresponds to a portion of the vasculature which includes a stenosis and which extends distally as far as resolution of said images allows.

19. The method according to claim 9, wherein said stenotic model corresponds to a portion of the vasculature which includes a stenosis, and said second model corresponds to a portion of the vasculature which does not include a stenosis and which is geometrically similar to the stenotic model.

20. The method according to claim 9, wherein said processing said images to produce said stenotic model and said second model comprises:

extending each of the models by one bifurcation;

calculating a new flow characteristic in each extended model;

updating said index responsively to said new flow characteristics and according to a predetermined criterion; and

iteratively repeating said extending, said calculating and said updating.

21. The method according to claim 9, wherein said index is calculated based on a ratio of the flow characteristic in the stenotic model to the flow characteristic in the second model.

22. The method according to claim 1, wherein said index is indicative of the need for revascularization.

23. The method according to claim 1, further comprising obtaining a Fractional Flow Ratio (FFR) based on said index.

24. The method according to claim 1, further comprising determining, based on said index, a ratio between maximal blood flow in an area of a stenosis and a maximal blood flow in a same area without stenosis.

25. The method according to claim 1, further comprising capturing said 2D images.

26. 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 receive a plurality of 2D images of a subject'"'"'s vasculature and execute the method according to claim 1.

27. A system for vascular assessment comprising:

a plurality of imaging devices configured for capturing a plurality of 2D images of a vasculature of a subject; and

a computer configured for receiving said plurality of 2D images and executing the method according to claim 1.