Automated lesion analysis based upon automatic plaque characterization according to a classification criterion

• US 7,627,156 B2
• Filed: 03/22/2007
• Issued: 12/01/2009
• Est. Priority Date: 03/22/2006
• Status: Active Grant

• Current Assignee: Volcano Corporation (Koninklijke Philips N.V.)
• Original Assignee: Volcano Corporation (Koninklijke Philips N.V.)
• Inventors: Nair, Anuja, Burgess, Vincent J., Vince, D. Geoffrey, Margolis, Marja Pauliina
• Primary Examiner(s): AZARIAN, SEYED H
• Application Number:
  US11/689,963
• Publication Number:
  US 20070260141A1
• Time in Patent Office: 985 Days
• Field of Search: 382/100, 382/128, 382/129, 382/130, 382/131, 382/132, 382/133, 382/134, 382/168, 382/170, 382/173, 382/191, 382/194, 382/199, 382/203, 382/224, 382/232, 382/258, 382/274, 382/276, 382/285, 382/291, 382/305, 382/312, 600/443, 600/549, 600/478, 600/427, 606/15, 607/15
• US Class Current: 382/128
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• 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 8/0833 involving detecting or locating foreign bodies or organic structures
• CPC Class Codes: A61B 8/12 in body cavities or body tracts, e.g. by using catheters catheters per se A61M25/00
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A system and method are disclosed for automatically classifying plaque lesions. A plaque classification application applies a plaque classification criterion to at least one graphical image, comprising a map of spectrally-analyzed characterized tissue of a vessel cross-section, to render an overall plaque classification for the slice or set of slices, covering a 3D volume. The plaque classification is based upon the amount and location of each characterized tissue type (e.g., necrotic core—NC). In an exemplary embodiment the set of potential plaque classifications, not to be confused with characterized tissue types—from which the plaque classifications are derived—include, for example: adaptive intimal thickening (AIT), pathological intimal thickening (PIT), fibroatheroma (FA), thin-cap fibroatheroma (TCFA), and fibro-calcific (FC).

Claims

1. A method of classifying plaque within a vessel, comprising:

rendering, by a tissue characterization application, a characterized tissue component map from a spectral analysis of ultrasound echo information for a vessel cross-section, wherein differing plaque components are distinguished, in the characterized tissue map, by assigned identifying values;

applying, by a plaque classification application, a classification criterion to spatially arranged data of the characterized tissue map, wherein the classification criterion includes a rule based upon a location, in relation to a lumen-plaque border, of confluent necrotic core within the vessel cross-section; and

rendering, in response to the applying step, a plaque classification associated with the vessel cross-section.

2. The method of claim 1 wherein the classification criterion renders a plaque classification for each vessel cross-section.

3. The method of claim 1 wherein the plaque classification application applies a set of hierarchically arranged classification rules.

4. The method of claim 3 wherein the rules are arranged as a decision tree.

5. The method of claim 3 wherein a top-level decision automatically identifies a cross-section having a relevant presence of plaque, wherein the relevant presence of plaque is determined at least based upon a minimum plaque thickness or stenosis.

6. The method of claim 1 wherein the classification criterion includes a rule based upon attachment of confluent dense calcium with confluent necrotic core.

7. The method of claim 1 wherein the classification criterion further includes a rule based on a percentage of fibro-fatty material present in a sample.

8. The method of claim 1 further comprising the step of rendering a graphical representation of a vessel segment displayed according to plaque classifications assigned to cross-sections of the vessel segment.

9. A computer-readable medium including computer-executable instructions facilitating classifying plaque within a vessel, the computer-executable instructions facilitating performing a set of steps comprising:

rendering a characterized tissue component map from a spectral analysis of ultrasound echo information for a vessel cross-section, wherein differing plaque components are distinguished, in the characterized tissue map, by assigned identifying values;

applying a classification criterion to spatially arranged data of the characterized tissue map, wherein the classification criterion includes a rule based upon a location, in relation to a lumen-plaque border, of confluent necrotic core within the vessel cross-section; and

rendering, in response to the applying step, a plaque classification associated with the vessel cross-section.

10. The computer-readable medium of claim 9 wherein the plaque classification application applies a set of hierarchically arranged classification rules.

11. The computer-readable medium of claim 10 wherein the rules are arranged as a decision tree.

12. The computer-readable medium of claim 10 wherein a top-level decision automatically identifies a cross-section having a relevant presence of plaque, wherein the relevant presence of plaque is determined at least based upon a minimum plaque thickness or stenosis.

13. The computer-readable medium of claim 9 wherein the classification criterion renders a plaque classification for each vessel cross-section.

14. The computer-readable medium of claim 9 wherein the classification criterion includes a rule based upon attachment of confluent dense calcium with confluent necrotic core.

15. The computer-readable medium of claim 9 wherein the classification criterion further includes a rule based on a percentage of fibro-fatty material present in a sample.

16. The computer-readable medium of claim 9 further comprising the step of rendering a graphical representation of a vessel segment displayed according to plaque classifications assigned to cross-sections of the vessel segment.

17. A system for classifying plaque within a vessel, the system comprising:

a tissue characterization application for rendering a characterized tissue component map from a spectral analysis of ultrasound echo information for a vessel cross-section, wherein differing plaque components are distinguished, in the characterized tissue map, by assigned identifying values;

a plaque classification application for applying a classification criterion to spatially arranged data of the characterized tissue map, and rendering a plaque classification associated with the vessel cross-section, wherein the classification criterion includes a rule based upon a location, in relation to a lumen-plaque border, of confluent necrotic core within the vessel cross-section and wherein the classification criterion includes a rule based upon the presence of confluent necrotic core.

18. The system of claim 17 wherein the classification criterion renders a plaque classification for each vessel cross-section.

19. The system of claim 17 wherein the plaque classification application applies a set of hierarchically arranged classification rules.

20. The system of claim 19 wherein the rules are arranged as a decision tree.

21. The system of claim 19 wherein a top-level decision automatically identifies a cross-section having a relevant presence of plaque, wherein the relevant presence of plaque is determined at least based upon a minimum plaque thickness or stenosis.

22. The system of claim 17 wherein the classification criterion includes a rule based upon attachment of confluent dense calcium with confluent necrotic core.

23. The system of claim 17 wherein the classification criterion further includes a rule based on a percentage of fibro-fatty material present in a sample.

24. The system of claim 17 wherein the plaque classification application further comprises a graphical output processor that renders a graphical representation of a vessel segment displayed according to plaque classifications assigned to cross-sections of the vessel segment.