Method of measuring intracranial pressure

• US 6,245,027 B1
• Filed: 09/20/1999
• Issued: 06/12/2001
• Est. Priority Date: 04/10/1998
• Status: Expired due to Term

• Current Assignee: Noam Alperin
• Original Assignee: Noam Alperin
• Inventors: Alperin, Noam
• Primary Examiner(s): Winakur, Eric F.
• Assistant Examiner(s): WINGOOD, PAMELA LYNN
• Application Number:
  US09/399,566
• Time in Patent Office: 631 Days
• Field of Search: 600/437, 600/454, 600/458, 600/465, 600/561
• US Class Current: 600/561
• CPC Class Codes: A61B 5/031 Intracranial pressure
• CPC Class Codes: A61B 5/407 Evaluating the spinal cord A61B5/4896 takes precedence
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• CPC Class Codes: G01R 33/563 of moving material, e.g. flow contrast angiography

Abstract

A method and apparatus are provided for finding a fluid conduit of a human body within a set of images collected over a portion of a cardiac cycle and where each image of the set of images is formed by a plurality of image elements. The method includes the steps of selecting a reference image element within the fluid conduit from the plurality of image elements, determining a velocity profile of each of the image elements over the cardiac cycle and identifying the fluid conduit by correlating the velocity profile of the reference element with velocity profiles at least some of the other image elements.

Claims

1. A method for identifying a fluid conduit of a human body within a set of images collected over a portion of a cardiac cycle and where each image of the set of images is formed by a plurality of image elements, such method comprising the steps of:

selecting a reference image element within the fluid conduit from the plurality of image elements;

determining a velocity profile of each of the image elements over the cardiac cycle; and

identifying the fluid conduit by correlating the velocity profile of the reference element with velocity profiles of at least some of the other image elements.

2. A method for identifying a fluid conduit of a human body within a set of images collected over a portion of a cardiac cycle and where each image of the set of images is formed by a plurality of image elements, such method comprising the steps of:

collecting 32 images over the cardiac cycle as the set of images;

selecting a reference image element within the fluid conduit from the plurality of image elements;

determining a velocity profile of each of the image elements over the cardiac cycle; and

identifying the fluid conduit by correlating the velocity profile of the reference element with velocity profiles of at least some of the other image elements.

3. A method for locating the fluid conduit of a human body within a set of images collected over a portion of a cardiac cycle and where each image of the set of images is formed by a plurality of image elements, such method comprising the steps of:

selecting a reference image element within the fluid conduit from the plurality of image elements using pixels as image elements;

determining a velocity profile of each of the image elements over the cardiac cycle; and

identifying the fluid conduit by correlating the velocity profile of the reference element with velocity profiles of at least some of the other image elements.

4. The method for identifying the fluid conduit as in claim 3 further comprising calculating a cross-correlation value for each pixel.

5. The method for identifying the fluid conduit as in claim 4 further comprising solving for the cross-correlation value by evaluating the equation $P_{\mathrm{XY}}=\frac{\sum _{}^{}}{}$

k=0N

 



(Rk-R_)

(XY-XY_)∑

k=1N

 



(R-R_)2

∑

k=1N

 



(XY-XY_)2,

where P_XY is the cross-correlation value at pixel location XY, R is the reference waveform, k is a time index of the cardiac cycle and N is a total number of images in the cardiac cycle.

6. The method for identifying the fluid conduit as in claim 4 further comprising selecting a correlation threshold between the cross-correlation value of the reference pixel and the other pixels.

7. The method for identifying the fluid conduit as in claim 6 further comprising selecting the correlation threshold from a region of a relatively constant number of pixels that have values above a selected threshold value.

8. The method for identifying the fluid conduit as in claim 7 further comprising forming an outline of the fluid conduit based upon the correlated velocity profiles.

9. The method for identifying the fluid conduit as in claim 8 wherein the step of finding the fluid conduit further comprises identifying a group of adjacent pixels of the pixels with correlated velocity profiles.

10. The method for identifying the fluid conduit as in claim 9 further comprising identifying a locus of points surrounding the grouped pixels.

11. Apparatus for identifying a fluid conduit of a human body within a set of images collected over a portion of a cardiac cycle and where each image of the set of images is formed by a plurality of image elements, such apparatus comprising:

means for selecting a reference image element within the fluid conduit from the plurality of image elements;

means for determining a velocity profile of each of the image elements over the cardiac cycle; and

means for identifying the fluid conduit by correlating the velocity profile of the reference element with velocity profiles of at least some of the other image elements.

12. Apparatus for identifying a fluid conduit of a human body within a set of images collected over a portion of a cardiac cycle and where each image of the set of images is formed by a plurality of image elements, such apparatus comprising:

means for collecting 32 images over the cardiac cycle as the set of images;

means for selecting a reference image element within the fluid conduit from the plurality of image elements;

means for determining a velocity profile of each of the image elements over the cardiac cycle; and

means for identifying the fluid conduit by correlating the velocity profile of the reference element with velocity profiles of at least some of the other image elements.

13. Apparatus for identifying a fluid conduit of a human body within a set of images collected over a portion of a cardiac cycle and where each image of the set of images is formed by a plurality of image elements, such apparatus comprising:

means for selecting a reference image element within the fluid conduit from the plurality of image elements using pixels as image elements;

means for determining a velocity profile of each of the image elements over the cardiac cycle; and

means for identifying the fluid conduit by correlating the velocity profile of the reference element with velocity profiles of at least some of the other image elements.

14. The apparatus for identifying the fluid conduit as in claim 13 further comprising means for calculating a cross-correlation value for each pixel.

15. The apparatus for locating the fluid conduit as in claim 14 further comprising means for solving for the cross-correlation value by evaluating the equation $P_{\mathrm{XY}}=\frac{\sum _{}^{}}{}$

k=0N

 



(Rk-R_)

(XY-XY_)∑

k=1N

 



(R-R_)2

∑

k=1N

 



(XY-XY_)2,

where P_XY is the cross-correlation value at pixel location XY, R is the reference waveform, k is a time index of the cardiac cycle and N is a total number of images in the cardiac cycle.

16. The apparatus for locating the fluid conduit as in claim 14 further comprising means for selecting a correlation threshold between the cross-correlation value of the reference pixel and the other pixels.

17. The apparatus for identifying the fluid conduit as in claim 16 further comprising means for selecting the correlation threshold from a region of a relatively constant number of pixels that have values above a selected threshold value.

18. The apparatus for identifying the fluid conduit as in claim 17 further comprising means for forming an outline of the fluid conduit based upon the correlated velocity profiles.

19. The apparatus for identifying the fluid conduit as in claim 18 wherein the means for finding the fluid conduit further comprises means for identifying a group of adjacent pixels of the pixels with correlated velocity profiles.

20. The apparatus for identifying the fluid conduit as in claim 19 further comprising means for identifying a locus of points surrounding the grouped pixels.

21. Apparatus for identifying a fluid conduit of a human body within a set of images collected over a portion of a cardiac cycle and where each image of the set of images is formed by a plurality of image elements, such apparatus comprising:

a pointer adapted to select a reference image element within the fluid conduit from the plurality of image elements;

an velocity processor adapted to determine a velocity profile of each of the image elements over the cardiac cycle; and

a correlation processor adapted to identify the fluid conduit by correlating the velocity profile of the reference element with velocity profiles of at least some of the other image elements.

22. The apparatus for identifying the fluid conduit as in claim 21 further comprising an image compiler adapted to collect 32 images over the cardiac cycle.

23. The apparatus for identifying the fluid conduit as in claim 21 further comprising a pixel processor adapted to use pixels as image elements.

24. The apparatus for identifying the fluid conduit as in claim 23 further comprising a cross-correlation processor adapted to calculate a cross-correlation value for each pixel.

25. The apparatus for identifying the fluid conduit as in claim 24 wherein the cross-correlation processor further comprising an arithmetic unit adapted to solve for the cross-correlation value by evaluating the equation $P_{\mathrm{XY}}=\frac{\sum _{}^{}}{}$

k=0N

 



(Rk-R_)

(XY-XY_)∑

k=1N

 



(R-R_)2

∑

k=1N

 



(XY-XY_)2,

where P_XY is the cross-correlation value at pixel location XY, R is the reference waveform, k is a time index of the cardiac cycle and N is a total number of images in the cardiac cycle.

26. The apparatus for identifying the fluid conduit as in claim 24 further comprising a threshold processor adapted to select a correlation threshold between the cross-correlation value of the reference pixel and the other pixels.

27. The apparatus for identifying the fluid conduit as in claim 26 further comprising a derivative processor adapted to select the correlation threshold from a region of a relatively constant number of pixels that have values above a selected threshold value.

28. The apparatus for identifying the fluid conduit as in claim 27 further comprising an outline processor adapted to form an outline of the fluid conduit based upon the correlated velocity profiles.

29. The apparatus for identifying the fluid conduit as in claim 28 wherein the outline processor finding the fluid conduit further comprises a comparison processor adapted to identify a group of adjacent pixels of the pixels with correlated velocity profiles.

30. The apparatus for identifying the fluid conduit as in claim 29 further comprising a locus processor adapted to identify a locus of points surrounding the grouped pixels.

31. A method for segmentation of a region that exhibits a specific dynamic behavior that is different from a surrounding region and for determination of a boundary of a spinal cord from a set of images collected over a portion of a cardiac cycle and where each image of the set of images is formed by a plurality of image elements, such method comprising the steps of:

selecting a reference image element within the spinal cord from the plurality of image elements;

determining a velocity profile of each of the image elements over the cardiac cycle; and

identifying the image elements that comprise the spinal cord by correlating the velocity profile of the reference element with velocity profiles of the other image elements.

32. The method for segmentation as in claim 31 further comprising determining an optimal cross correlation threshold based upon a relative number of pixels that have values above a threshold value as a function of that threshold value.

33. An apparatus for segmentation of a region that exhibits a specific dynamic behavior that is different from a surrounding region and for determination of a boundary of a spinal cord from a set of images collected over a portion of a cardiac cycle and where each image of the set of images is formed by a plurality of image elements, such apparatus comprising:

means for selecting a reference image element within the spinal cord from the plurality of image elements;

means for determining a velocity profile of each of the image elements over the cardiac cycle; and

means for identifying the image elements that comprise the spinal cord by correlating the velocity profile of the reference element with velocity profiles of the other image elements.

34. The apparatus for segmentation as in claim 33 further comprising means for determining an optimal cross correlation threshold based upon a relative number of pixels that have values above a threshold value as a function of that threshold value.

35. An apparatus for segmentation of a region that exhibits a specific dynamic behavior that is different from a surrounding region and for determination of a boundary of a spinal cord from a set of images collected over a portion of a cardiac cycle and where each image of the set of images is formed by a plurality of image elements, such apparatus comprising:

a pointer adapted to select a reference image element within the spinal cord from the plurality of image elements;

a velocity processor adapted to determine a velocity profile of each of the image elements over the cardiac cycle; and

a correlation processor adapted to identify the image elements that comprise the spinal cord by correlating the velocity profile of the reference element with velocity profiles of the other image elements.

36. The apparatus for segmentation as in claim 35 further comprising a derivative processor adapted to determine an optimal cross correlation threshold based upon a relative number of pixels that have values above a threshold value as a function of that threshold value.