METHOD AND SYSTEM FOR DETECTION OF OBSTRUCTIONS IN VASCULATURE

• US 20090012382A1
• Filed: 07/02/2007
• Published: 01/08/2009
• Est. Priority Date: 07/02/2007
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Mendonca, Paulo Ricardo Dos Santos, Dutta, Sandeep, Suryanarayanan, Srikanth, Gopinath, Ajay, Roy, Arunabha Shasanka
• Application Number:
  US11/772,654
• Publication Number:
  US 20090012382A1
• Time in Patent Office: Days
• US Class Current: 600/407
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/489 Blood vessels
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• 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: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

A method for automatic detection of obstructions in vasculature in an anatomical region is presented. The method includes partitioning the anatomical region into a plurality of sub-regions based at least in part on anatomical knowledge. Further, the method includes adaptively computing a threshold intensity value corresponding to each of the plurality of sub-regions. Additionally, the method includes extracting the vasculature in each of the plurality of sub-regions based on the corresponding computed threshold intensity value, where the extracted vasculature comprises a plurality of vessel segments. The method also includes detecting an obstruction in the extracted vasculature. Systems and computer-readable medium that afford functionality of the type defined by this method is also contemplated in conjunction with the present technique.

Claims

1. A method for automatic detection of obstructions in vasculature in an anatomical region, the method comprising:

partitioning the anatomical region into a plurality of sub-regions based at least in part on anatomical knowledge;

adaptively computing a threshold intensity value corresponding to each of the plurality of sub-regions;

extracting the vasculature in each of the plurality of sub-regions based on the corresponding computed threshold intensity value, wherein the extracted vasculature comprises a plurality of vessel segments; and

detecting an obstruction in the extracted vasculature.

2. The method of claim 1, wherein the obstruction in the vasculature comprises an embolus, calcification, plaque, or a combination thereof.

3. The method of claim 1, further comprising obtaining image data from a data source, wherein the image data is representative of the anatomical region, wherein the data source comprises a data stream or archived data, and wherein the archived data is obtained from a first storage.

4. The method of claim 3, wherein the data source comprises an imaging system, and wherein the imaging system comprises one of a computed tomography imaging system, a magnetic resonance imaging system, an X-ray imaging system, or a combination thereof.

5. The method of claim 1, wherein partitioning the anatomical region into a plurality of sub-regions based at least in part on anatomical knowledge comprises partitioning the anatomical region into a plurality of sub-regions based on a distance from a predetermined location in the anatomical region.

6. The method of claim 1, further comprising separating the anatomical region from surrounding background.

7. The method of claim 1, wherein detecting the obstruction comprises identifying local statistics corresponding to each of the plurality of vessel segments.

8. The method of claim 7, wherein the local statistics comprises an intensity value, an intensity distribution, a local intensity histogram, mean intensity, standard deviation of intensities, texture features, shape, size parameters, local contrast, gradients, morphometry, or a combination thereof.

9. The method of claim 8, wherein identifying local statistics comprises:

defining a centerline corresponding to each of the plurality of vessel segments in the extracted vasculature;

identifying contrast intensity values along the centerlines of each of the plurality of vessel segments; and

determining size parameters corresponding to each of the plurality of vessel segments.

10. The method of claim 9, further comprising labeling each of the plurality of vessel segments with the corresponding identified contrast intensity values.

11. The method of claim 9, further comprising:

obtaining a predefined intensity distribution model, a predefined morphometry model, or both, from a second storage; and

comparing the identified intensity values associated with each of the vessel segments with a corresponding predefined intensity distribution model, the size parameters associated with each of the vessel segments with a corresponding predefined morphometry model, or both, to facilitate detection of the obstruction.

12. The method of claim 11, further comprising storing data corresponding to the detected obstruction in a third storage.

13. The method of claim 11, further comprising generating a user-viewable representation of the detected obstruction data.

14. A method for automatic detection of obstructions in vasculature in a lung region, the method comprising:

adaptively partitioning the anatomical region into a plurality of sub-regions based on a distance from a predetermined location in the anatomical region;

adaptively computing a threshold intensity value corresponding to each of the plurality of sub-regions;

extracting the vasculature in each of the plurality of sub-regions based on the corresponding computed threshold intensity value, wherein the extracted vasculature comprises a plurality of vessel segments; and

detecting an obstruction in the extracted vasculature.

15. The method of claim 14, wherein the predetermined location comprises a hilum region in the lung region.

16. The method of claim 14, further comprising separating the lung region from surrounding background.

17. The method of claim 14, wherein detecting the obstruction comprises:

defining a centerline corresponding to each of the plurality of vessel segments in the extracted vasculature;

identifying contrast intensity values along the centerlines of each of the plurality of vessel segments; and

determining size parameters corresponding to each of the plurality of vessel segments.

18. The method of claim 17, further comprising labeling each of the plurality of vessel segments with the corresponding identified contrast intensity values.

19. The method of claim 18, further comprising:

obtaining a predefined intensity distribution model, a predefined morphometry model, or both, from a second storage; and

comparing the identified intensity values associated with each of the vessel segments with a corresponding predefined intensity distribution model, the size parameters associated with each of the vessel segments with a corresponding predefined morphometry model, or both, to facilitate detection of the obstruction.

20. The method of claim 19, further comprising generating a user-viewable representation of the detected obstruction data.

21. A computer readable medium comprising one or more tangible media, wherein the one or more tangible media comprise:

code adapted to partition the anatomical region into a plurality of sub-regions based on a distance from a predetermined location in the anatomical region;

code adapted to adaptively compute a threshold intensity value corresponding to each of the plurality of sub-regions;

code adapted to extract the vasculature in each of the plurality of sub-regions based on the corresponding computed threshold intensity value, wherein the extracted vasculature comprises a plurality of vessel segments; and

code adapted to detect an obstruction in the extracted vasculature.

22. The computer readable medium, as recited in claim 21, wherein the code adapted to detect the obstruction comprises:

code adapted to define a centerline corresponding to each of the plurality of vessel segments in the extracted vasculature;

code adapted to identify contrast intensity values along the centerlines of each of the plurality of vessel segments; and

code adapted to determine size parameters corresponding to each of the plurality of vessel segments.

23. The computer readable medium, as recited in claim 22, further comprising code adapted to label each of the plurality of vessel segments with the corresponding identified contrast intensity values.

24. The computer readable medium, as recited in claim 23, further comprising:

code adapted to obtain a predefined intensity distribution model, a predefined morphometry model, or both, from a second storage; and

code adapted to compare the identified intensity values associated with each of the vessel segments with a corresponding predefined intensity distribution model, the size parameters associated with each of the vessel segments with a corresponding predefined morphometry model, or both, to facilitate detection of the obstruction.

25. A detection system, comprising:

an obstruction detection platform configured to detect one or more obstructions in vasculature of an anatomical region, wherein the obstruction detection platform is configured to;

partition the anatomical region into a plurality of sub-regions based on a distance from a predetermined location in the anatomical region;

adaptively compute a threshold intensity value corresponding to each of the plurality of sub-regions;

extract the vasculature in each of the plurality of sub-regions based on the corresponding computed threshold intensity value, wherein the extracted vasculature comprises a plurality of vessel segments; and

detect an obstruction in the extracted vasculature.

26. The system of claim 25, further configured to generate a user-viewable representation of the detected obstruction data.

27. An imaging system, comprising:

an acquisition subsystem configured to acquire image data, wherein the image data is representative of an anatomical region;

a processing subsystem in operative association with the acquisition subsystem and comprising an obstruction detection platform configured to;

partition the anatomical region into a plurality of sub-regions based on a distance from a predetermined location in the anatomical region;

adaptively compute a threshold intensity value corresponding to each of the plurality of sub-regions;

extract the vasculature in each of the plurality of sub-regions based on the corresponding computed threshold intensity value, wherein the extracted vasculature comprises a plurality of vessel segments; and

detect an obstruction in the extracted vasculature.