Automated wall motion quantification in aortic dissections

• US 8,170,307 B2
• Filed: 09/23/2008
• Issued: 05/01/2012
• Est. Priority Date: 09/23/2008
• Status: Expired due to Fees

• Current Assignee: Methodist Hospital
• Original Assignee: Methodist Hospital
• Inventors: Karmonik, Christof, Lumsden, Alan B.
• Primary Examiner(s): TANG, MINH NHUT
• Application Number:
  US12/284,594
• Publication Number:
  US 20100074494A1
• Time in Patent Office: 1,316 Days
• Field of Search: None
• US Class Current: 382/128
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/30104 Vascular flow; Blood flow; Perfusion
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume

Abstract

A computer implemented method of processing MRI images to determine one or more characteristics of an anatomical feature that includes obtaining one or more first MRI images of the anatomical feature; identifying a boundary of an anatomical feature in the MRI images; and using the identified boundary to mask one or more corresponding second MRI images of the anatomical feature to isolate the anatomical feature within the second MRI images.

Claims

1. A computer implemented method of processing MRI images to determine one or more characteristics of an anatomical feature, comprising:

obtaining one or more first MRI images of the anatomical feature;

identifying a boundary of the anatomical feature in the first MRI images;

using the identified boundary to mask one or more corresponding second MRI images of the anatomical feature to isolate the anatomical feature within the second MRI images;

determining by a computer an average displacement for a center of the anatomical feature;

determining by the computer an average displacement for each boundary point of the identified boundary over time; and

using by the computer the average displacement for each boundary point to determine a total average displacement of the identified boundary.

2. The method of claim 1, wherein the anatomical feature comprises one or more of the following:

a lumen of an aorta or a lumen of an aneurysm.

3. The method of claim 1, wherein the first MRI images comprise MRI magnitude images.

4. The method of claim 1, wherein the second MRI images comprise MRI phase images.

5. The method of claim 1, wherein the anatomical feature comprises a lumen;

and wherein identifying a boundary of the lumen in the second MRI images comprises determining a contiguous area within the second MRI images having increased fluid flow relative to other areas in the second MRI images.

6. The method of claim 1, further comprising:

identifying the center of the anatomical feature.

7. The method of claim 1, further comprising:

determining a volumetric flow rate within the identified boundary of the anatomical feature.

8. The method of claim 7, wherein determining a volumetric flow rate within the identified boundary of the anatomical feature comprises multiplying an average pixel intensity level within the identified boundary by the cross sectional area of the identified boundary.

9. The method of claim 1,

wherein the average displacement for the center or the average displacement for each boundary point is determined during a cardiac cycle.

10. The method of claim 1, further comprising:

determining a volumetric flow rate through the anatomical feature over time; and

correlating the average displacement for the center or the average displacement for each boundary point with the volumetric flow rate.

11. The method of claim 1, further comprising:

determining a maximum distention of the identified boundary of the anatomical feature over time.

12. The method of claim 11, further comprising:

determining a maximum contraction of the identified boundary of the anatomical feature over time.

13. The method of claim 1, further comprising:

determining a maximum contraction of the identified boundary of the anatomical feature over time.

14. A computer readable medium storing a computer program for processing MRI images to determine one or more characteristics of an anatomical feature, the computer program when executed causing a computer to:

obtain one or more first MRI images of the anatomical feature;

identify a boundary of the anatomical feature in the first MRI images;

use the identified boundary to mask one or more corresponding second MRI images of the anatomical feature to isolate the anatomical feature within the second MRI images;

determine an average displacement for a center of the anatomical feature over time;

determine an average displacement for each boundary point of the identified boundary over time; and

use the average displacement for each boundary point to determine a total average displacement of the identified boundary.

15. The computer readable medium of claim 14, wherein the anatomical feature comprises one or more of the following:

a lumen of an aorta or a lumen of an aneurysm.

16. The computer readable medium of claim 14, wherein the first MRI images comprise MRI magnitude images.

17. The computer readable medium of claim 14, wherein the second MRI images comprise MRI phase images.

18. The computer readable medium of claim 14, wherein the anatomical feature comprises a lumen;

and wherein identifying a boundary of the lumen in the second MRI images comprises determining a contiguous area within the second MRI images having increased fluid flow relative to other areas in the second MRI images.

19. The computer readable medium of claim 14, the computer program when executed further causing the computer to:

identify the center of the anatomical feature.

20. The computer readable medium of claim 14, the computer program when executed further causing the computer to:

determine a volumetric flow rate within the identified boundary of the anatomical feature.

21. The computer readable medium of claim 20, wherein determining a volumetric flow rate within the identified boundary of the anatomical feature comprises multiplying an average pixel intensity level within the identified boundary by the cross sectional area of the identified boundary.

22. The computer readable medium of claim 14,

wherein the average displacement for the center or the average displacement for each boundary point is determined during a cardiac cycle.

23. The computer readable medium of claim 14, the computer program when executed further causing the computer to:

determine a volumetric flow rate through the anatomical feature over time; and

correlate the average displacement for the center or the average displacement for each boundary point with the volumetric flow rate.

24. The computer readable medium of claim 14, the computer program when executed further causing the computer to:

determine a maximum distention of the identified boundary of the anatomical feature over time.

25. The computer readable medium of claim 24, the computer program when executed further causing the computer to:

determine a maximum contraction of the identified boundary of the anatomical feature over time.

26. The computer readable medium of claim 14, the computer program when executed further causing the computer to:

determine a maximum contraction of the identified boundary of the anatomical feature over time.

27. A computer implemented method for processing MRI images, comprising:

displaying an MRI image including an anatomical feature;

calculating a boundary of the anatomical feature using the MRI image;

displaying the calculated boundary of the anatomical feature overlayed onto the MRI image;

determining by a computer an average displacement for a center of the anatomical feature over time;

determining by the computer an average displacement for each boundary point of the calculated boundary of the anatomical feature over time; and

using by the computer the average displacement for each boundary point to determine a total average displacement of the calculated boundary.

28. The method of claim 27, wherein the MRI image comprises a time averaged MRI image.

29. The method of claim 28, wherein the calculated boundary of the anatomical feature overlayed on the MRI image comprises a time sequence of calculated boundaries.

30. The method of claim 27, wherein the boundary of the calculated anatomical feature overlayed on the MRI image comprises a time sequence of calculated boundaries.

31. A computer readable medium storing a computer program for processing MRI images, the computer program when executed causing a computer to:

display an MRI image including an anatomical feature;

calculate a boundary of the anatomical feature using the MRI image;

display the calculated boundary of the anatomical feature overlayed onto the MRI image;

determine an average displacement for a center of the anatomical feature over time;

determine an average displacement for each boundary point of the calculated boundary of the anatomical feature over time; and

use the average displacement for each boundary point to determine a total average displacement of the calculated boundary.

32. The computer readable medium of claim 31, wherein the MRI image comprises a time averaged MRI image.

33. The computer readable medium of claim 32, wherein the calculated boundary of the anatomical feature overlayed on the MRI image comprises a time sequence of calculated boundaries.

34. The computer readable medium of claim 31, wherein the calculated boundary of the anatomical feature overlayed on the MRI image comprises a time sequence of calculated boundaries.

35. A computer implemented method for processing MRI images to diagnose an acute aortic dissection in a patient, comprising:

obtaining an MRI image of the aorta of the patient;

determining by a computer an average displacement for a center of the aorta;

calculating a boundary of the lumen of the aorta MRI image; and

determining by the computer an average displacement for each boundary point of the lumen boundary;

using by the computer the average displacement for each boundary point to determine a degree of total average displacement of the lumen boundary of the MRI image of the aorta during a cardiac cycle; and

if the degree of total average displacement is greater than a threshold value, then diagnosing that the patient has an acute aortic dissection.

36. The method of claim 35, wherein the average displacement for each boundary point comprises contractions and distentions of the lumen boundary.

37. A computer readable medium storing a computer program for processing MRI images to diagnose an acute aortic dissection in a patient, the computer program when executed causing a computer to:

obtain an MRI image of the aorta of the patient;

determine an average displacement for a center of the aorta;

calculate a boundary of the lumen of the aorta MRI image during the cardiac cycle; and

determine an average displacement for each boundary point of the lumen boundary;

use the average displacement for each boundary point to determine a degree of total average displacement of the lumen boundary of the MRI image of the aorta during the cardiac cycle; and

if the degree of total average displacement is greater than a threshold value, then diagnose that the patient has an acute aortic dissection.

38. The computer readable medium of claim 37, wherein the average displacement for each boundary point comprises contractions and distentions of the lumen boundary.

39. A computer implemented method for processing MRI images to diagnose a cerebral aneurysm in a patient, comprising:

obtaining an MRI image of the cerebral aneurysm of the patient;

determining by a computer an average displacement for a center of the cerebral aneurysm over time;

calculating a boundary of the lumen of the cerebral aneurysm MRI image; and

determining by the computer an average displacement for each boundary point of the lumen boundary;

using by the computer the average displacement for each boundary point to determine a degree of total average displacement of the lumen boundary of the MRI image of the cerebral aneurysm during a cardiac cycle; and

if the degree of total average displacement is greater than a threshold value, then diagnosing that the patient has the cerebral aneurysm.

40. The method of claim 39, wherein the average displacement for each boundary point comprises contractions and distentions of the lumen boundary.

41. A computer readable medium storing a computer program for processing MRI images to diagnose a cerebral aneurysm in a patient, the computer program when executed causing a computer to:

obtain an MRI image of the cerebral aneurysm of the patient;

determine an average displacement for a center of the cerebral aneurysm over time;

calculate a boundary of the lumen of the cerebral aneurysm MRI image during the cardiac cycle; and

determine by the computer an average displacement for each boundary point of the lumen boundary;

use the average displacement for each boundary point to determine a degree of total average displacement of the lumen boundary of the MRI image of the cerebral aneurysm during the cardiac cycle; and

if the degree of total average displacement is greater than a threshold value, then diagnose that the patient has the cerebral aneurysm.

42. The computer readable medium of claim 41, wherein the average displacement for each boundary point comprises contractions and distentions of the lumen boundary.

43. A computer implemented method of processing MRI images to determine one or more characteristics of an anatomical feature, comprising:

obtaining one or more first MRI images of the anatomical feature;

filtering by a computer the first MRI images using a spatial bandpass filter to generate filtered MRI images;

filtering by the computer the filtered MRI images using a median filter to generate MRI magnitude images;

identifying a boundary of the anatomical feature in the MRI magnitude images;

using the identified boundary to mask one or more corresponding second MRI images of the anatomical feature to isolate the anatomical feature within the second MRI images;

determining a volumetric flow rate through the anatomical feature over time;

determining by the computer a displacement for a center of the anatomical feature at each cross section over time;

determining by the computer an average displacement for the anatomical feature based on the displacement for the center at each cross section;

correlating the average displacement for the anatomical feature with the volumetric flow rate;

determining by the computer a maximum distention and a maximum contraction for each boundary point of the identified boundary over time;

determining by the computer an average displacement for each boundary point of the identified boundary over time;

correlating the average displacement for each boundary point with the volumetric flow rate by determining a localized motion of the identified boundary based on the maximum distention and the maximum contraction for each boundary point; and

using by the computer the average displacement for each boundary point to determine a total average displacement of the identified boundary.

44. The method of claim 43, wherein the anatomical feature comprises one or more of the following:

a lumen of an aorta or a lumen of an aneurysm.

45. The method of claim 43, wherein the second MRI images comprise MRI phase images.

46. The method of claim 43, wherein the anatomical feature comprises a lumen;

and wherein identifying a boundary of the lumen in the second MRI images comprises determining a contiguous area within the second MRI images having increased fluid flow relative to other areas in the second MRI images.

47. The method of claim 43, further comprising:

identifying the center of the anatomical feature.

48. The method of claim 43, wherein determining a volumetric flow rate through the anatomical feature comprises multiplying an average pixel intensity level within the identified boundary by the cross sectional area of the identified boundary.

49. The method of claim 43, wherein the average displacement for the center or the average displacement for each boundary point is determined during a cardiac cycle.

50. A computer readable medium storing a computer program for processing MRI images, the computer program when executed causing a computer to:

obtain one or more first MRI images of the anatomical feature;

filter by a computer the first MRI images using a spatial bandpass filter to generate filtered MRI images;

filter by the computer the filtered MRI images using a median filter to generate MRI magnitude images;

identify a boundary of the anatomical feature in the MRI magnitude images;

use the identified boundary to mask one or more corresponding second MRI images of the anatomical feature to isolate the anatomical feature within the second MRI images;

determine a volumetric flow rate through the anatomical feature over time;

determine by the computer a displacement for a center of the anatomical feature at each cross section over time;

determine by the computer an average displacement for the anatomical feature based on the displacement for the center at each cross section;

correlate the average displacement for the anatomical feature with the volumetric flow rate;

determine by the computer a maximum distention and a maximum contraction for each boundary point of the identified boundary over time;

determine by the computer an average displacement for each boundary point of the identified boundary over time;

correlate the average displacement for each boundary point with the volumetric flow rate by determining a localized motion of the identified boundary based on the maximum distention and the maximum contraction for each boundary point; and

use the average displacement for each boundary point to determine by the computer a total average displacement of the identified boundary.

51. The computer readable medium of claim 50, wherein the anatomical feature comprises one or more of the following:

a lumen of an aorta or a lumen of an aneurysm.

52. The computer readable medium of claim 50, wherein the second MRI images comprise MRI phase images.

53. The computer readable medium of claim 50, wherein the anatomical feature comprises a lumen;

and wherein identifying a boundary of the lumen in the second MRI images comprises determining a contiguous area within the second MRI images having increased fluid flow relative to other areas in the second MRI images.

54. The computer readable medium of claim 50, the computer program when executed further causing the computer to:

identify the center of the anatomical feature.

55. The computer readable medium of claim 50, wherein determining a volumetric flow rate through the anatomical feature comprises multiplying an average pixel intensity level within the identified boundary by the cross sectional area of the identified boundary.

56. The computer readable medium of claim 50, wherein the average displacement for the center or the average displacement for each boundary point is determined during a cardiac cycle.