Surgical assistance planning method using lung motion analysis

• US 9,020,229 B2
• Filed: 05/13/2011
• Issued: 04/28/2015
• Est. Priority Date: 05/13/2011
• Status: Active Grant

• Current Assignee: Broncus Medical Incorporated
• Original Assignee: Broncus Medical Incorporated
• Inventors: Rai, Lav, Gibbs, Jason David, Wibowo, Henky
• Primary Examiner(s): Bitar, Nancy
• Application Number:
  US13/107,471
• Publication Number:
  US 20120288173A1
• Time in Patent Office: 1,446 Days
• Field of Search: None
• US Class Current: 382/131
• CPC Class Codes: A61B 6/5264 due to motion
• CPC Class Codes: G06T 17/00 Three dimensional [3D] modelling, e.g. data description of 3D objects
• CPC Class Codes: G06T 2207/10076 4D tomography; Time-sequential 3D tomography
• CPC Class Codes: G06T 2207/30061 Lung
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments

Abstract

A medical analysis method for estimating a motion vector field of the magnitude and direction of local motion of lung tissue of a subject is described. In one embodiment a first 3D image data set of the lung and a second 3D image data set is obtained. The first and second 3D image data sets correspond to images obtained during inspiration and expiration respectively. A rigid registration is performed to align the 3D image data sets with one another. A deformable registration is performed to match the 3D image data sets with one another. A motion vector field of the magnitude and direction of local motion of lung tissue is estimated based on the deforming step. The motion vector field may be computed prior to treatment to assist with planning a treatment as well as subsequent to a treatment to gauge efficacy of a treatment. Results may be displayed to highlight.

Claims

1. A medical method for observing the local motion of a medical implement in a lung of a subject comprising the steps of:

a) receiving first image data that includes a lung tissue of the lung in a first state;

b) receiving second image data that includes the lung tissue of the lung in a second state different than the first state;

c) rigidly registering the first image data and the second image data with one another using an anatomical reference wherein the anatomical reference excludes the lung tissue and is a relatively fixed anatomical structure having minimal deflection between breathing states of the lung;

d) based on the anatomical reference from the rigidly registering step, deforming at least one of the following;

i) the first image data of the lung to match the second image data of the lung, and ii) the second image data of the lung to match the first image data of the lung by moving voxels in at least one of the first image data or the second image data until voxel intensity values of the first and second image data are matched; and

e) generating a motion vector field of the magnitude and direction of local motion of the lung tissue based on the rigidly registering step and deforming step wherein the local motion of the lung tissue between first and second states is independent of the anatomical reference defined in the rigidly registering step.

2. The method of claim 1 further comprising receiving a 3D location of a candidate medical implement.

3. The method of claim 2 further comprising computing displacement of the candidate medical implement based on the motion vector field.

4. The method of claim 3 further comprising displaying the displacement of the medical implement.

5. The method of claim 4 wherein said medical implement is selected from the group consisting of a medical implant and a medical device.

6. The method of claim 5 wherein the medical implement is a fiducial marker.

7. The method of claim 1 wherein the anatomical reference is a spinal column.

8. The method of claim 1 wherein the first state corresponds to the lung during an inspiration.

9. The method of claim 3 further comprising placing a selected medical implement in the lung of the subject subsequent to computing the displacement of the candidate medical implement.

10. The method of claim 9 further comprising displaying an animation of the medical implement as it is displaced.

33. The method of claim 1 wherein the deformation is performed by minimizing a cost function.

34. The method of claim 33 wherein minimizing the cost function is performed by minimizing a sum-of-squared-differences.

11. A medical method for observing the efficacy of a region in the lung of a subject comprising the steps of:

a) receiving first image data of the lung in a first state;

b) receiving second image data of the lung in a second state;

c) registering the first image data and the second image data with one another using a plurality of types of anatomical reference features and wherein each of the anatomical reference features exclude the lung tissue and have minimal deflection between the first and second states;

d) deforming at least one of the following;

i) the first image data of the lung to match the second image data of the lung, and ii) the second image data of the lung to match the first image data of the lung; and

e) generating a first motion vector field of the magnitude and direction of local motion of lung tissue based on the deforming step wherein the local motion of the lung tissue between first and second states is independent of the anatomical references defined in the rigidly registering step.

12. The method of claim 11 further comprising:

receiving third image data of the lung during a third state;

receiving fourth image data of the lung in a fourth state;

registering the third image data and the fourth image data with one another using an anatomical feature of the subject;

deforming at least one of the following;

i) the third image data of the lung to match the fourth image data of the lung, and ii) the fourth image data of the lung to match the third image data of the lung; and

generating a second motion vector field of the magnitude and direction of local motion of lung tissue based on the deforming step.

13. The method of claim 12 further comprising automatically computing the difference between the first motion vector field and the second motion vector field.

14. The method of claim 13 wherein said second motion vector field is generated following a medical procedure performed on the subject.

15. The method of claim 14 wherein the medical procedure is a surgical procedure.

16. The method of claim 15 wherein the medical procedure is based on applying radiation to a region of interest.

17. The method of claim 14 wherein the medical procedure is based on creating openings in the airway walls of the lung, the parenchyma, or removing a ROI.

18. The method of claim 11 further comprising receiving a candidate region in the lung.

19. The method of claim 18 further comprising displaying the displacement of the candidate region based on the first motion vector field.

20. The method of claim 19 further comprising placing a virtual fiducial marker in the lung and observing displacement of the virtual fiducial marker based on the first motion vector field.

21. The method of claim 11 wherein at least one of the plurality of reference features is selected from the group consisting of the spine and airway branches.

22. The method of claim 11 wherein the first state corresponds to the lung during an inspiration by the subject.

23. The method of claim 11 wherein the first motion vector field corresponds to respiratory motion of the lung.

24. The method of claim 11 further comprising sending to a display the first motion vector field.

25. A medical analysis method for estimating a motion vector field of the magnitude and direction of local motion of lung tissue of a subject comprising the steps of:

a) receiving first image data of the lung in a first state;

b) receiving second image data of the lung in a second state;

c) identifying an anatomical reference wherein the anatomical reference is a fixed anatomical structure and excludes lung tissue;

d) registering the first image data and the second image data with one another using the fixed anatomical reference;

d) deforming at least one of the following;

i) the first image data of the lung to match the second image data of the lung, and ii) the second image data of the lung to match the first image data of the lung; and

e) estimating a motion vector field of the magnitude and direction of local motion of lung tissue based on the deforming step wherein the local motion of the lung tissue between first and second states is relative to an anatomical reference defined in the rigidly registering step.

26. The method of claim 25 wherein the anatomical reference is a spinal column.

27. The method of claim 25 wherein the first state corresponds to the lung during an inspiration by the subject.

28. The method of claim 25 further comprising receiving a 3D location of a candidate medical implement.

29. The method of claim 28 further comprising computing displacement of the candidate medical implement based on the motion vector field.

30. The method of claim 29 further comprising displaying the displacement of the medical implement.

31. A medical analysis method for estimating a motion vector field of the magnitude and direction of local motion of lung tissue of a first subject comprising the steps of:

a) receiving first image data of the lung of the first subject in a first state;

b) generating an atlas motion vector field, wherein the step of generating the atlas motion vector field comprises;

receiving first image data of the lung of a second subject in a first state;

receiving second image data of the lung of the second subject in a second state;

registering the first image data of the second subject and the second image data of the second subject with one another using a reference feature;

deforming at least one of the following;

i) the first image data of the second subject to match the second image data of the second subject, and ii) the second image data of the second subject to match the first image data of the second subject; and

estimating the atlas motion vector field based on the deforming step; and

c) estimating the local motion of the lung of the first subject based on the atlas motion vector field.

32. The method of claim 31 wherein the first image data of the first subject comprises a set of 2D CT images of the lung.