Patient-specific coronary territory mapping

• US 8,010,175 B2
• Filed: 05/03/2005
• Issued: 08/30/2011
• Est. Priority Date: 05/05/2004
• Status: Expired due to Fees

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Williams, James P., O'Donnell, Thomas
• Primary Examiner(s): JAWORSKI, FRANCIS J
• Application Number:
  US11/120,876
• Publication Number:
  US 20050272992A1
• Time in Patent Office: 2,310 Days
• Field of Search: 600/374, 600/407, 600423-425, 600/447, 600/450, 600508-509, 382/131, 382/154, 382/294, 382/128, 382/254, 382/256, 382/276, 382/285, 382/298, 345/420, 345581-588, 345629-630, 345653-654, 703/11
• US Class Current: 600/407
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30048 Heart; Cardiac
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models

Abstract

A system and method for patient-specific coronary territory mapping are provided, the system including an adapter unit for receiving three-dimensional (“3D”) image data, a modeling unit in signal communication with the adapter unit for fitting a geometric model to the received data and segmenting coronary vessels from the received data, and a mapping unit in signal communication with the modeling unit for mapping the segmented vessels to the surface of the model; and the method including receiving 3D image data, fitting a geometric model to the received data, segmenting coronary vessels from the received data, and mapping the segmented vessels to the surface of the model.

Claims

1. A method for patient-specific coronary territory mapping comprising performing the following with a processor:

receiving three-dimensional (“

3D”

) image data;

fitting a geometric model to the received data;

segmenting coronary vessels from the received data;

mapping the segmented vessels to the surface of the model;

computing a Voronoi diagram responsive to the mapping on an epicardial surface; and

dividing the surface based on proximity to the source arteries,

wherein the model is based on at least one of population and nonuniform rational B-splines (“

NURB”

) and using the patient-specific coronary territory mapping model to determine if medical intervention is needed in the patient.

2. A method as defined in claim 1 wherein the model is based on population, finite element analysis (“

FEA”

), and nonuniform rational B-splines (“

NURB”

).

3. A method as defined in claim 1, further comprising:

receiving data indicative of a default left ventricular (“

LV”

) model; and

dividing the surface of the default LV based on coronary structures.

4. A method as defined in claim 3 wherein the 3D image data is X-ray Angiography data.

5. A method as defined in claim 1 wherein:

the geometric model is indicative of a left ventricle; and

the extracted vessels include an anterior artery, a circumflex artery, a coronary artery, and a main artery.

6. A method as defined in claim 1, further comprising labeling the model via competitive region growing to provide a patient-specific coronary map.

7. A method as defined in claim 1, further comprising noninvasive tomographic imaging to provide the image data.

8. A method as defined in claim 1 wherein the mapping identifies the coronary artery most responsible for the perfusion of any point on a myocardium.

9. A method as defined in claim 1 wherein the mapping is a function of the proximity of each vessel along an epicardial surface and prior knowledge of general perfusion patterns in myocardia.

12. A method as defined in claim 1, further comprising:

sampling the resulting 3D mapping to create a two-dimensional (“

2D”

) mapping for any arbitrary image orientation.

13. A method as defined in claim 1 wherein the mapping relates regions of a myocardium to actual arteries, including primary and branch vessels.

14. A method as defined in claim 1, further comprising subdividing the vessels into segments before and after an occlusion.

15. A method as defined in claim 1, wherein the mapping comprises:

projecting segmented vessels onto the epicardial surface of the Left Ventricle (“

LV”

); and

expanding the multiple projections simultaneously over the surface to form perfusion regions.

16. A method as defined in claim 15 wherein the resulting 3D coronary distribution mapping is based on tomographic images, and the method further comprises resampling the mapping for display in an arbitrary image orientation.

17. A method as defined in claim 15, wherein projecting the segmented vessels comprises discretizing the vessel path and finding the closest point on the epicardial surface for each point along the path.

18. A method as defined in claim 1, further comprising labeling the entire surface of the model such that each point on the surface is associated with the vessel most likely responsible for its irrigation.

19. A method as defined in claim 1 wherein mapping is indicative of at least one region being irrigated by the closest vessel, the historical probability of at least one region being irrigated by a particular vessel, and a correlation of physical data for at least one particular vessel.

20. A method as defined in claim 1, further comprising fusing the patient-specific coronary territory mapping with cardiac function mapping to provide additional information.

21. A method as defined in claim 1, further comprising providing patient-specific parametric maps indicative of myocardial viability.

22. A method as defined in claim 1, further comprising visually encoding cardiac parameters in accordance with the left ventricular shape to consolidate information from tomographic images into a surface display for ease of viewing.

10. A method for patient-specific coronary territory mapping comprising performing the following with a processor:

receiving three-dimensional (“

3D”

) image data;

fitting a geometric model to the received data;

segmenting coronary vessels from the received data; and

mapping the segmented vessels to the surface of the model,

wherein said mapping is a function of the proximity of each vessel along an epicardial surface and prior knowledge of perfusion patterns in myocardia, and the prior knowledge is indicative of septums that lack a supply from close primary vessels and using the patient-specific coronary territory mapping model to determine if medical intervention is needed in the patient.

11. A method as defined in claim 10, further comprising:

computing a Voronoi diagram responsive to the mapping on an epicardial surface; and

dividing the surface based on proximity to the source arteries,

wherein the model is based on nonuniform rational B-splines (“

NURB”

).

23. A method for patient-specific coronary territory mapping comprising performing the following with a processor:

receiving three-dimensional (“

3D”

) image data;

fitting a geometric model to the received data;

segmenting coronary vessels from the received data;

mapping the segmented vessels to the surface of the model;

labeling the model via competitive region growing to provide a patient-specific coronary map; and

dividing the surface of the model into at least three territories in accordance with proximities to the segmented vessels,

wherein the model is based on at least one of population and nonuniform rational B-splines (“

NURB”

) and using the patient-specific coronary territory mapping model to determine if medical intervention is needed in the patient.

24. A method for patient-specific coronary territory mapping comprising performing the following with a processor:

receiving three-dimensional (“

3D”

) image data;

fitting a geometric model to the received data;

segmenting coronary vessels from the received data;

mapping the segmented vessels to the surface of the model; and

labeling the entire surface of the model such that each point on the surface is associated with the vessel most likely responsible for irrigation,

wherein the labeling comprises competitive region growing, each group of points during competitive region growing expands simultaneously over the surface and halts when the front of one group meets the front of another group, and the model is based on at least one of population and nonuniform rational B-splines (“

NURB”

) and using the patient-specific coronary territory mapping model to determine if medical intervention is needed in the patient.

25. A method for patient-specific coronary territory mapping comprising performing the following with a processor:

receiving three-dimensional (“

3D”

) image data;

fitting a geometric model to the received data;

segmenting coronary vessels from the received data;

mapping the segmented vessels to the surface of the model; and

computing an average territory map for a large population of subjects, the map including confidence values associated with the region assignments,

wherein the model is based on at least one of population and nonuniform rational B-splines (“

NURB”

) and using the patient-specific coronary territory mapping model to determine if medical intervention is needed in the patient.

26. A method for patient-specific coronary territory mapping comprising performing the following with a processor:

receiving three-dimensional (“

3D”

) image data;

fitting a geometric model to the received data;

segmenting coronary vessels from the received data;

mapping the segmented vessels to the surface of the model; and

labeling the entire surface of the model such that each point on the surface is associated with the vessel most likely responsible for its irrigation,

wherein regions not directly perfused are excluded from labeling, and the model is based on at least one of population and nonuniform rational B-splines (“

NURB”

) and using the patient-specific coronary territory mapping model to determine if medical intervention is needed in the patient.

27. A method for patient-specific coronary territory mapping comprising performing the following with a processor:

receiving three-dimensional (“

3D”

) image data;

fitting a geometric model to the received data;

segmenting coronary vessels from the received data;

mapping the segmented vessels to the surface of the model; and

labeling the entire surface of the model such that each point on the surface is associated with the vessel most likely responsible for its irrigation,

wherein labeling comprises a random walk simulation, and the model is based on at least one of population and nonuniform rational B-splines (“

NURB”

) and using the patient-specific coronary territory mapping model to determine if medical intervention is needed in the patient.

28. A method for patient-specific coronary territory mapping comprising performing the following with a processor:

receiving three-dimensional (“

3D”

) image data;

fitting a geometric model to the received data;

segmenting coronary vessels from the received data;

mapping the segmented vessels to the surface of the model;

projecting the segmented vessels onto the epicardial surface of a left ventricle by discretizing the vessel path and finding the closest point on the epicardial surface for each point along the path; and

expanding the multiple projections simultaneously over the surface to form perfusion regions,

wherein portions of vessels farther than a threshold distance from the left ventricular epicardial surface are excluded to remove segments not responsible for irrigating the left ventricle, and the model is based on at least one of population and nonuniform rational B-splines (“

NURB”

) and using the patient-specific coronary territory mapping model to determine if medical intervention is needed in the patient.