Method for identification of anatomical landmarks

• US 20080123923A1
• Filed: 10/20/2006
• Published: 05/29/2008
• Est. Priority Date: 09/08/2006
• Status: Active Grant

• Current Assignee: Medtronic Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Incorporated (Medtronic Plc)
• Inventors: Gielen, Frans L.H., Appenrodt, Peter, Hertel, Frank, Gemmar, Peter
• Granted Patent: US 8,160,677 B2
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 7/32 using correlation-based methods
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 10/245 by locating a pattern; Special marks for positioning

Abstract

A method for determining anatomical landmarks is provided. The anatomical landmarks can be used to determine anatomical targets of a patient. The method may be used for planning a procedure and performing a procedure, such as performing a procedure in the brain.

Claims

1. A method of determining an anatomical landmark in an anatomy of a patient, comprising:

pre-processing image data of the anatomy to at least smooth the image data;

determining a seed portion of the image data;

determining a selected region based on the seed portion of the image data; and

identifying the anatomical landmark based at least in part on the determining of the selected region.

2. The method of claim 1 further comprising:

obtaining image data of the brain of the patient;

wherein obtaining image data of the patient includes at least one of obtaining a T1 weighted magnetic resonance image data, a T2 weighted magnetic resonance image data, ultrasound image data, X-ray image data, computed tomography image data, Positron emission tomography image data, or combinations thereof of the patient

3. The method of claim 1, further comprising:

providing a processor operable to execute instructions to determine a selected region based upon the seed portion.

4. The method of claim 1, wherein pre-processing the image data includes non-linear anisotropic diffusion of the image data.

5. The method of claim 1, wherein pre-processing the image data further includes at least one of reducing noise of the image data, maintaining mass in the image data, defining an edge, removing unneeded data, or combinations thereof in the image data.

6. The method of claim 1, wherein determining a seed portion of the image data includes identifying a pixel, a voxel, or combinations thereof defining a portion of a third ventricle of a brain.

7. The method of claim 6, wherein determining a seed portion includes a user identifying in the image data a seed portion.

8. The method of claim 1, wherein determining a selected region includes segmenting the image data based upon region growing to identify a third ventricle of a brain in the image data.

9. The method of claim 8, wherein region growing includes determining all of the portions of the image data that contact the seed portion of the image data that include characteristics similar to the seed portion.

10. The method of claim 1, wherein determining a selected region based on the seed portion includes:

determining whether an intensity in a seed voxel reaches into the test voxel within a threshold distance when the test voxel contacts the seed voxel;

adding the test voxel to the seed voxel to create a homogeneity region; and

finding each of the voxels in the image data that contact the homogeneity region that include an intensity that extends a distance into the test voxel.

11. The method of claim 10, further comprising:

providing a processor;

providing a storage medium operable to store the image data including information about a plurality of voxels in the image data;

executing instructions with the processor to determine whether the threshold distance has been exceeded in the test voxel; and

executing instructions with the processor to add a test voxel to a homogeneous region if the threshold distance is reached.

12. The method of claim 11, further comprising:

determining a line that includes ends that are substantially equal distance from the edge of the selected region in the image data; and

aligning a plurality of slices of image data based upon the determined line;

wherein aligning the image data includes substantially removing a slope from the line.

13. The method of claim 12, further comprising:

determining a brightest pixel in the image data of a brain that is located immediately inferior to a column of fornix in a mid-sagittal plane that is extracted from the image data based upon the line.

14. The method of claim 13 further comprising:

segmenting the image data to partition the image data into two classes including an object and a background to identify a cerebral aqueduct of the brain in the image data; and

identifying an area in the image data dorsal to the cerebral aqueduct as it interconnects with the determined third ventricle region.

15. The method of claim 1, wherein determining a selected region based on the seed portion includes:

determining whether a seed voxel that includes an intensity is within a threshold distance from the seed voxel;

adding the test voxel to the seed voxel to create a homogeneity region; and

finding each of the voxels in the image data that contact the homogeneity region that meet the intensity of the seed voxel within the threshold distance.

16. The method of claim 14, further comprising:

determining the position of a posterior commissure from the area in the image data.

17. The method of claim 1, further comprising:

determining the location of an anterior commissure and a posterior commissure in the image data of a brain.

18. The method of claim 17, further comprising:

determining an anatomical target based upon the determination of the anterior commissure and the posterior commissure.

19. The method of claim 18, wherein determining the anatomical target includes at least one of determining a location of the sub-thalamic nucleus based upon a predetermined location of the sub-thalamic nucleus relative to the anterior commissure and the posterior commissure;

moving a sensing device near the determined anatomical target to sense an activity near the determined anatomical target to refine an edge of the anatomical target;

or combinations thereof.

20. The method of claim 19, further comprising:

guiding an instrument to the sub-thalamic nucleus; and

stimulating the sub-thalamic nucleus.

21. A method of determining an anatomical landmark of a brain of a patient, comprising:

obtaining image data of the brain of the patient;

processing the image data of the brain of the patient;

determining a first region of the brain in the image data of the patient; and

determining a second region of the brain in the image data based at least in part upon the determining of the first region of the brain in the image data.

22. The method of claim 21 wherein obtaining the image data includes obtaining image data of the brain of the patient with a T1 weighted MRI, a T2 weighted MRI, a computed tomography scan, a positron emission tomography scan, an ultrasound, image data showing spatial or time varying constructs, or combinations thereof.

23. The method of claim 21 further comprising:

providing a processor operable to process the image data of the brain of the patient.

24. The method of claim 21, further comprising:

executing instructions with a processor to determine the first region of the brain in the image data; and

executing further instructions to determine the second region of the brain in the image data.

25. The method of claim 24, wherein the processor executes instructions to determine the first region of the brain in the image data by identifying test voxels in the image data that meet a threshold distance intensity when compared to a seed voxel;

and

determining a homogeneous region in the image data.

26. The method of claim 25, wherein the first region is at least one of a third ventricle, a mid-sagittal plane, an anterior commissure, a posterior commissure, or combinations thereof.

27. The method of claim 25, further comprising:

executing instructions to determine a skeletonized line including ends that are equal distance from an edge of the determined first region within the image data of the brain;

wherein determining the skeletonized line is based upon minimizing the homogeneous region until a region of minimally connected pixels is determined.

28. The method of claim 27, further comprising:

obtaining a plurality of slices to define the image data of the brain of the patient;

aligning the plurality of slices of image data based upon the skeletonized line in each of the image data slices; and

substantially removing the slope of the skeletonized line between each of the image slices of the image data.

29. The method of claim 28, wherein determining a second region of the brain from the image data includes extracting a mid-sagittal plane from the image data that is aligned with the skeletonized line.

30. The method of claim 29, further comprising:

determining a third region of the brain from the image data including determining a brightest pixel in the image data that is located immediately inferior to the column of the fornix in the mid-sagittal plane.

31. The method of claim 23, further comprising determining a portion of the image data associated with the brightest pixel as the anterior commissure.

32. The method of claim 31, further comprising:

determining a posterior commissure; and

determining an anatomical target based upon the determined location of the anterior commissure and the posterior commissure.

33. The method of claim 32, wherein the anatomical target is the sub-thalamic nucleus and the determined locations of the sub-thalamic nucleus is based upon the a predetermined location of the sub-thalamic nucleus relative to the anterior commissure and the posterior commissure.

34. The method of claim 33, further comprising:

guiding an instrument relative to the sub-thalamic nucleus; and

performing stimulation of the sub-thalamic nucleus.

35. The method of claim 29, further comprising:

determining a posterior commissure including segmenting a cerebral aqueduct by determining whether an object pixel has an intensity greater than a threshold brightness when compared to a background pixel; and

determining a brightest pixel just dorsal to the point where the cerebral aqueduct enters the determined first region.

36. The method of claim 35, further comprising:

determining a anterior commissure;

determining an anatomical target based upon the determined location of the anterior commissure and the posterior commissure.

37. The method of claim 36, wherein the anatomical target is the sub-thalamic nucleus and the determined locations of the sub-thalamic nucleus is based upon the a predetermined location of the sub-thalamic nucleus relative to the anterior commissure and the posterior commissure.

38. The method of claim 37, further comprising:

guiding an instrument relative to the sub-thalamic nucleus; and

performing stimulation of the sub-thalamic nucleus.

39. A method of determining an anatomical landmark of a brain of a patient, comprising:

obtaining image data of the brain including a plurality of slices of the brain of the patient;

processing the image data to at least one of smooth the image data, remove unneeded data from the image data, define edges in the image data, or combinations thereof;

determining a first region in the image data of the brain at least in part by segmenting the image data;

aligning each of the slices of the image data based upon the first region of the image data; and

determining a second region in the image data that defines a second region of the brain.

40. The method of claim 39, wherein obtaining the image data includes obtaining a plurality of T1 weighted magnetic resonance image data slices, a T2 weighted MRI, a computed tomography scan, a positron emission tomography scan, an ultrasound, image data showing spatial or time varying constructs, or combinations thereof of the brain.

41. The method of claim 39, wherein processing the image data includes at least one of applying a linear Gaussian filter, non-linear median filter, non-linear anisotropic diffusion, or combinations thereof to the obtained image data.

42. The method of claim 39, wherein segmenting the image data includes at least one of region growing, geometric active contour models, or combinations thereof.

43. The method of claim 39, wherein segmenting the image data includes:

defining a seed portion of the image data;

providing a processor;

executing instructions with the processor to compare a test voxel intensity to a threshold intensity;

executing instructions with the processor to add the test voxel to the seed voxel if the test voxel intensity is within the threshold intensity to begin the formation of a homogeneous region; and

identifying every test voxel that touches the growing homogeneous region until no further test voxels have the test voxel intensity within the threshold intensity.

44. The method of claim 43, further comprising:

determining a skeletonized line in the determined homogenous region with the processor;

wherein determining the skeletonized line includes;

executing instructions with the processor to successively thin the edges of the homogeneous region and stopping when a line of portions that are minimally connected includes ends that are substantially equal distant from the boundaries of the homogeneous regions created;

removing a slope between the plurality of slices of the brain of the patient based upon the skeletonized line; and

extracting a mid-sagital plane based upon a sagital plane formed by the plurality of skeleton lines in the plurality of slices.

45. The method of claim 44, further comprising:

providing the processor to determine the second region on the image data including;

determining an area in the image data located immediately inferior to the column of the fornix in the image data of the brain;

determining a brightest pixel in the located area immediately inferior to the column of the fornix in the same level as the mid-sagittal plane; and

determining the brightest pixel to be an anterior commissure of the brain in the image data.

46. The method of claim 45, further comprising:

determining a location of a posterior commissure in the image data; and

determining the location of an anatomical target based upon a predetermined location of the anatomical target to the anterior commissure and the posterior commissure.

47. The method of claim 46, further comprising;

guiding an instrument to the anatomical target; and

at least one of stimulating the anatomical target, providing a gene therapy to the target, providing a chemical therapy to the target;

providing a protein therapy to the target, or combinations thereof.

48. The method of claim 43, wherein determining the second region of the image data includes:

providing instructions to be executed by the processor to segment the image data at the posterior boundary of the determined mid-sagittal plane to determine an object portion from a background portion;

determining that the object portion is a cerebral aqueduct;

determining the area where the cerebral aqueduct opens into the defined first region in the image data;

determining the brightest portion just dorsal to the point where the cerebral aqueduct opens into the first region; and

determining that the brightest portion defines a point of the posterior commissure in the brain.

49. The method of claim 48, further comprising:

determining a location of an anterior commissure in the image data;

determining the location of an anatomical target based upon a predetermined location of the anatomical target to the anterior commissure and the posterior commissure.

50. The method of claim 49, further comprising;

guiding a deep brain stimulation probe to the anatomical target; and

stimulating the anatomical target with the deep brain stimulation probe.