AUTOMATED MEASUREMENT SYSTEM AND METHOD FOR CORONARY ARTERY DISEASE SCORING

• US 20150265162A1
• Filed: 10/24/2013
• Published: 09/24/2015
• Est. Priority Date: 10/24/2012
• Status: Active Grant

• Current Assignee: Cathworks Ltd.
• Original Assignee: Cathworks Ltd.
• Inventors: Lavi, Guy, Merhav, Uri
• Granted Patent: US 9,943,233 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2034/107 Visualisation of planned trajectories or target regions
• CPC Class Codes: A61B 2576/00 Medical imaging apparatus involving image processing or analysis A61B1/00009, A61B6/52 and A61B8/52 take precedence; image processing per se G06T; details of algorithms for analysing medical images G06T7/0012
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• 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 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 6/504 for diagnosis of blood vessels, e.g. by angiography
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: A61B 8/0891 for diagnosis of blood vessels
• 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: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• 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

Automated image analysis used in vascular disease characterization. Coronary vasculature in particular is automatically characterized in some embodiments for lesion complexity and related anatomical and/or functional parameters related to disease state. In some embodiments, a “virtual revascularization” model is used as a reference in disease state determinations. In some embodiments, disease parameters are determined for application by a disease characterization tool such as a SYNTAX Score calculator.

Claims

1. A method of producing a mammalian vascular state score applicable to predict surgical intervention outcome, the method comprising:

receiving vascular image data including;

relative positions in one or more two-dimensional angiographic images of a plurality of vascular segments linked by branch points, and at least one lesion for which a respective lesion complexity includes a degree of occlusion, and a position relative to the vascular segments;

determining automatically a plurality of lesion complexity-related vascular metrics for each of a plurality of said vascular segments, based on at least one angiographic image of said received image data;

determining said lesion complexity for use as an at least one subscore of a vascular state scoring tool;

wherein the lesion complexity is based on said plurality of vascular metrics, and on said position of the lesion within the vascular segments and relative to the branch points, the position including a determined number of vascular segments that the lesion comprises; and

calculating said vascular state score based on said at least one subscore.

2. The method of claim 1, wherein said determining of said lesion complexity-related vascular metrics comprises determination of a vascular width metric function for the imaged state of said vascular segment, and determination of a corresponding width metric function for a modeled state of said vascular segment distinct from said imaged state.

3. The method of claim 1, wherein said vascular metrics comprise a metric which is a function of vascular segment position.

5. The method of claim 1, wherein said vascular image data images the arterial vasculature of the heart.

6. The method of claim 1, wherein said vascular state scoring tool is SYNTAX Score.

8. The method of claim 1, wherein said at least one subscore comprises at least half of all image data-based subscores comprised within said vascular state scoring tool.

9. The method of claim 1, wherein said determining of said vascular metrics comprises mapping angiographic image data measurement values to positions within said plurality of vascular segments.

10. The method of claim 9, wherein said positions are examinable to determine position relative to branch points among said plurality of vascular segments.

11. The method of claim 9, wherein said positions are associated with anatomically identifying vascular segment labels.

12. The method of claim 10, wherein said vascular metrics comprise measurements of vascular segment stenosis.

15. The method of claim 1, wherein said determining of vascular metrics comprises determining a vascular model representation for said plurality of vascular segments based on said received vascular image data.

16. The method of claim 15, wherein said determining of vascular metrics comprises determining an unstenosed model representation for said plurality of vascular segments based on said received image data.

17. The method of claim 16, wherein said determining of vascular metrics comprises determining a severity of stenosis in said plurality of vascular segments based on differences between said unstenosed model representation and said vascular model representation.

19. The method of claim 1, wherein said vascular state scoring tool is a modification of the SYNTAX Score scoring tool.

27. The method of claim 1, comprising:

determining to perform percutaneous angioplasty in an imaged patient based on said received output;

wherein

at the time of said determination, the patient remains catheterized from the imaging procedure producing said received vascular image data.

4. (canceled)

7. (canceled)

13-14. -14. (canceled)

18. (canceled)

20-26. -26. (canceled)

28. A system for automatic calculation of a vascular state score of lesion complexity applicable to predict surgical intervention outcome, comprising:

a subscore extractor functionally connectable to at least one two-dimensional angiographic vascular image data source, configured to;

determine at least one vascular lesion complexity-related metric based on two-dimensional angiographic vascular image data received from said image data source, and

determine at least one lesion complexity parameter based on said at least one metric; and

a vascular state score calculator, configured to;

receive a plurality of lesion complexity parameters from said subscore extractor, and compose said subscores into a vascular state score of lesion complexity.

29. The system of claim 28, wherein said subscore extractor comprises a metrics extractor, operable to receive said vascular image data and extract from it said at least one lesion complexity-related metric.

30. The system of claim 29, wherein said metrics extractor comprises a vascular tree reconstructor, operable to reconstruct a data structure representing a connected group of vascular segments from said vascular image data.

31. The system of claim 30, wherein said data structure represents branch connection relationships among said connected group of vascular segments.

33. The system of claim 29, wherein said metrics extractor comprises a metrics module, operable to determine for a vascular segment one or more morphometric functions along the length of said segment.

32. (canceled)

34-50. -50. (canceled)