System and method for vascular border detection

• US 20050196026A1
• Filed: 04/29/2004
• Published: 09/08/2005
• Est. Priority Date: 03/04/2004
• Status: Active Grant

• Current Assignee: Cleveland Clinic Foundation
• Original Assignee: Cleveland Clinic Foundation
• Inventors: Nair, Anuja, Vince, D. Geoffrey, Klingensmith, Jon D., Kuban, Barry D.
• Granted Patent: US 7,215,802 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 8/0858 involving measuring tissue layers, e.g. skin, interfaces
• CPC Class Codes: A61B 8/12 in body cavities or body tracts, e.g. by using catheters catheters per se A61M25/00

Abstract

A system and method is provided for using the frequency spectrum of a radio frequency (RF) signal backscattered from vascular tissue to identify at least one border (e.g., tissue interface, etc.) on a vascular image. Embodiments of the present invention operate in accordance with a data gathering device (e.g., an intra-vascular ultrasound (IVUS) device, etc.) electrically connected to a computing device and a transducer via a catheter. The transducer is used to gather radio frequency (RF) data backscattered from vascular tissue. The RF data is then provided to (or acquired by) the computing device via the data-gathering device. In one embodiment of the present invention, the computing device includes (i) at least one data storage device (e.g., database, memory, etc.) for storing a plurality of tissue types and parameters related thereto and (ii) at least one application (e.g., a characterization application, a gradient-border application, a frequency-border application and/or an active-contour application). The characterization application is used to convert (or transform) the RF data into the frequency domain and to identify a plurality of parameters associated therewith. The identified parameters are then compared to the parameters stored in the data storage device to identify the corresponding tissue type. This information (e.g., tissue type, corresponding RF data, etc.) is then used, either alone or together with other border-related information (e.g., gradient information, other-border information, etc.), to determine at least one border on a vascular image.

Claims

1. A method of identifying a border on an image of a vascular object, comprising:

acquiring RF data backscattered from vascular tissue;

transforming said RF data into the frequency domain;

identifying a plurality of parameters of said transformed RF data;

using said plurality of parameters and previously stored data to identify at least a first and second portion of said transformed RF data that corresponds to a blood and non-blood portion of said vascular tissue, respectively; and

determining said border on said image of said vascular object in accordance with said first and second portions of said transformed RF data.

2. The method of claim 1, wherein said second portion of said vascular tissue corresponds to a plaque portion of said vascular tissue.

3. The method of claim 1, wherein said step of determining said border further comprises determining the luminal border on said image of said vascular object in accordance with said first and second portions of said transformed RF data.

4. The method of claim 3, wherein said steps of (i) using said plurality of parameters and previously stored data to identify a least a first and second portion of said transformed RF data and (ii) determining said border further comprises:

using said plurality of parameters and previously stored data to identify at least a third portion of said transformed RF data that corresponds to at least a third tissue type of said vascular tissue, said third tissue type being selected from a list of tissue types consisting of medial tissue and adventitial tissue; and

determining the medial-adventitial border on said image of said vascular object in accordance with said second and third portion of said transformed RF data.

5. The method of claim 4, wherein said third tissue type corresponds to both medial and adventitial tissues.

6. The method of claim 1, wherein said step of determining said border further comprises determining said border on said image of said vascular object in accordance with gradient information, said gradient information being derived from said RF data.

7. The method of claim 1, further comprising the step of using other-border data from at least one other image of said vascular object to approximate said border on said image of said vascular object.

8. The method of claim 7, wherein said step of determining said border further comprises determining said border on said image of said vascular object in accordance with gradient information, said gradient information being derived from said RF data.

9. The method of claim 6, wherein said gradient information further comprises gradient-force data and gradient-border data.

10. The method of claim 1, further comprising the step of filtering said first and second portions of said transformed RF data before they are used to determine said border.

11. The method of claim 10, wherein said step of filtering further comprises filtering said second portion of said transformed RF data to reduce the amount of non-blood particles visible in an image of said second portion of said transformed RF data.

12. The method of claim 11, wherein said step of filtering further comprises filtering said first portion of said transformed RF data to reduce the number of tissue types visible in an image of said first portion of said transformed RF data.

13. The method of claim 1, wherein said step of determining said border further comprises determining said border on said image of said vascular object in accordance with spectral-force data and spectral-border data, said spectral-force data and said spectral-border data being derived from said transformed RF data.

14. The method of claim 1, wherein said step of determining said border further comprises determining said border on said image of said vascular object in accordance with at least one algorithm, said at least one algorithm being selected from a list of algorithms consisting of a continuity algorithm, a curvature algorithm, and a relatedness algorithm.

15. The method of claim 1, wherein said step of transforming said RF data further comprises transforming said RF data into the autoregressive (AR) frequency power spectrum.

16. The method of claim 1, wherein said step of identifying a plurality of parameters further comprises identifying at least one parameter of said transformed RF data, said at least one parameter being selected from a list of parameters consisting of maximum power, minimum power, frequency at maximum power, frequency at minimum power, y intercept, slope, mid-band fit, and integrated backscatter.

17. The method of claim 1, wherein said step of acquiring RF data further comprises acquiring RF data that is both backscattered from said vascular tissue and gated to electrocardiogram (ECG) information.

18. The method of claim 1, wherein said step of identifying a plurality of parameters of said transformed RF data further comprises identifying at least one parameter of said RF data.

19. The method of claim 18, wherein said at least one parameter of said RF data comprises tissue depth.

20. A border-identification system comprising:

a computing device adapted to be electrically connected to a data-gathering device and to acquire from said data-gathering device RF data backscattered from vascular tissue, said computing device comprising;

a database for storing a plurality of parameters and a plurality of corresponding tissue types;

a characterization application electrically connected to said database and adapted to;

transform said RF data into the frequency domain;

identify a plurality of parameters associated with said transformed RF data; and

use said plurality of parameters and information stored in said database to identify at least two tissue types of said vascular tissue, wherein a portion of said transformed RF data corresponds to said at least two tissue types;

a gradient-border application adapted to use said RF data to determine gradient information;

a frequency-border application adapted to use at least said portion of said transformed RF data to determine spectral information; and

an active-contour application adapted to use said gradient information and said spectral information to determine at least one border on an image of a vascular object comprising said vascular tissue.

21. The border-identification system of claim 20, further comprising a memory for storing other-border information, said active-contour application using said other-border information, said gradient information and said spectral information to determine said at least one border.

22. The border-identification system of claim 20, wherein said data-gathering device comprises an intra-vascular ultrasound (IVUS) console.

23. The border-identification system of claim 20, wherein said database is further adapted to store at least one parameter, said at least one parameter being selected from a list of parameters consisting of maximum power, minimum power, frequency at maximum power, frequency at minimum power, y intercept, slope, mid-band fit, and integrated backscatter.

24. The border-identification system of claim 20, wherein said database is further adapted to link a portion of said plurality of parameters to one of said plurality of tissue types, said one of said plurality of tissue types being selected from a list of tissue types consisting of blood and plaque.

25. The border-identification system of claim 20, wherein said at least two tissue types comprise blood and plaque tissues.

26. The border-identification system of claim 20, wherein said at least two tissue types are selected from a list of tissue types consisting of plaque, medial and adventitial tissues.

27. The border-identification system of claim 20, wherein said gradient information further comprises gradient-border data and said active-contour application is further adapted to use said gradient-border data to determine said at least one border on said image of said vascular object, wherein said gradient-border data is based on said RF data and represents an approximation of said at least one border.

28. The border-identification system of claim 20, wherein said spectral information further comprises spectral-border data and said active-contour application is further adapted to use said spectral-border data to determine said at least one border on said image of said vascular object, wherein said spectral-border data is based on at least said portion of said transformed RF data and represents an approximation of said at least one border.

29. The border-identification system of claim 21, wherein said other-border information corresponds to at least one border from at least one proceeding image of said vascular object.

30. The border-identification system of claim 21, wherein said other-border information corresponds to a plurality of borders from a plurality of images of said vascular object.

31. The border-identification system of claim 20, wherein said active-contour application is further adapted to determine said at least one border on said image of said vascular object by considering the continuity and the curvature of said at least one border.

32. The border-identification system of claim 20, wherein said active-contour application is further adapted to determine the luminal and medial-adventitial borders on said image of said vascular object.

33. The border-identification system of claim 32, wherein said active-contour application is further adapted to determine said luminal and medial-adventitial borders on said image of said vascular object such that said luminal border is always inside said medial-adventitial border.

34. The border-identification system of claim 20, wherein said gradient information further comprises gradient-force data and said active-contour application is further adapted to use said gradient-force data to determine said at least one border on said image of said vascular object.

35. The border-identification system of claim 20, wherein said spectral information further comprises spectral-force data and said active-contour application is further adapted to use said spectral-force data to determine said at least one border on said image of said vascular object.

36. The border-identification system of claim 20, wherein said characterization application is further adapted to identify at least one parameter associated with said RF data.

37. The border-identification system of claim 36, wherein said at least one parameter associated with said RF data comprises tissue depth.

38. A method of identifying at least one boundary on a vascular image, comprising:

acquiring RF data backscattered from vascular tissue;

transforming said RF data into the frequency domain;

identifying at least one parameter of said transformed RF data;

using said at least one parameter and previously stored data to identify at least one tissue type and transformed RF data corresponding thereto (corresponding RF data);

using said RF data to determine gradient information pertaining to said at least one boundary on said vascular image; and

using at least said gradient information and said corresponding RF data to determine said at least one boundary on said vascular image.

39. The method of claim 38, wherein said step of using at least said gradient information and said corresponding RF data further comprises using at least said gradient information and said transformed RF data to determine said at least one boundary on said vascular image.

40. The method of claim 38, wherein said at least one parameter is selected from a list of parameters consisting of maximum power, minimum power, frequency at maximum power, frequency at minimum power, y intercept, slope, mid-band fit, and integrated backscatter.

41. The method of claim 38, wherein said step of using said gradient information and said corresponding RF data further comprises using other-boundary data to determine said at least one boundary on said vascular image.

42. The method of claim 41, further comprising the step of using said corresponding RF data to determine spectral-force data and spectral-boundary data, wherein at least said spectral-force data, said spectral-boundary data, and said gradient information are used to determine said at least one boundary on said vascular image.

43. The method of claim 41, further comprising the step of using said gradient information to determine gradient-force data and gradient-border data, wherein at least said gradient-force data, said gradient-boundary data and corresponding RF data are used to determine said at least one boundary on said vascular image.

44. The method of claim 42, further comprising the step of using said gradient information to determine gradient-force data and gradient-border data, wherein at least said gradient-force data, said spectral-force data, said gradient-border data, and said spectral-border data are used to determine said at least one boundary on said vascular image.

45. The method of claim 42, further comprising the step of filtering said corresponding RF data before it is used to determine said spectral-force data and said spectral-boundary data.