Systems and methods for detecting intracranial pressure and volume
It has been discovered that even mild changes in cerebrospinal fluid (CSF) pressure or intracranial pressure (ICP) can be detected immediately as evidenced by distortions in the ONS surface structure. Further, the changes in the ONS persist after the CSF pressure has returned to normal. The stability of ONS distortions provides a means of detecting transient changes in brain pressure even when the use of the diagnostic ultrasound is delayed. One embodiment provides systems and methods for detecting or diagnosing brain injury by detecting distortions or deformations of the ONS, preferably using ultrasound.
1. A method for detecting traumatic brain injury in a subject comprising the steps of:
obtaining a plurality of images of the subject's Optic Nerve Sheath (ONS) along the length of the subject's ONS that extends outwardly behind the eye globe, the images being obtained at a tissue penetration depth of between 3 mm and 25 mm behind the eye globe;
creating a three-dimensional (3D) model of a segment of the subject's ONS from said plurality of images, wherein the 3D model defines at least a surface contour and a volume of the 3D modeled segment;
measuring from the 3D model the surface contour and the volume of the 3D modeled segment to determine a present state of the subject's ONS; and
comparing the subject's present state measured ONS surface contour and volume to a baseline state 3D model surface contour and volume in order to identify present distortions of the subject's ONS surface contour and volume and to thereby characterize a present state of the subject's ONS and detect a current or recent traumatic brain injury.
2. The method of claim 1 , wherein the step of obtaining a plurality of images comprises the steps of;
obtaining a series of transverse ONS images;
registering alignment of the transverse ONS images;
reformatting the registered transverse ONS images to coronal slices;
segmenting ONS contours from the coronal slices; and
creating a 3D volumetric model from the segmented ONS contours.
3. The method of claim 2 , wherein the surface contour of the 3D modeled segment of the ONS is measured as a variability of the surface contour of the 3D modeled segment of the ONS.
4. The method of claim 3 , wherein variability of the surface contour is measured by measuring radial lengths from a centerline of the 3D modeled segment of the ONS to multiple points on an outer surface of the 3D modeled segment of the ONS.
5. The method of claim 3 , wherein variability of the surface contour is measured by measuring lengths from a common point on a centerline of the 3D modeled segment of the ONS to multiple points on an outer surface of the 3D modeled segment of the ONS.
6. The method of claim 5 wherein the variability is measured over time.
7. The method of claim 3 further comprising the step of:
providing a line graph of Optical Nerve Sheath Diameter (ONSD) measurements along a length of the 3D modeled segment of the ONS; and
analyzing changes in ONSD, wherein changes in ONSD along a length of the 3D modeled segment of the ONS is indicative of brain injury.
8. The method of claim 3 wherein variability of the surface contour is measured by measuring length measurements from a fixed depth at a midline of the 3D modeled segment of the ONS to multiple points on the outer surface of the 3D modeled segment of the ONS.
9. The method of claim 8 wherein the variability is measured over time.
10. The method of claim 1 , wherein the surface contour of the 3D modeled segment of the ONS is measured as a variability of the surface contour.
11. The method of claim 10 , wherein variability of the surface contour of the 3D modeled segment of the ONS is measured by measuring radial lengths from a centerline of 3D modeled segment of the ONS to multiple points on an outer surface of the 3D modeled segment.
12. The method of claim 10 , wherein variability of the surface contour is measured by measuring lengths from a common point on a centerline of the 3D modeled segment of the ONS to multiple points on an outer surface of the 3D modeled segment.
13. The method of claim 10 wherein the variability is measured over time.
14. The method of claim 10 wherein variability of the surface contour is measured by measuring length measurements from a fixed depth at a midline of the 3D modeled segment of the ONS to multiple points on the outer surface of the 3D modeled segment of the ONS.
15. The method of claim 14 wherein the variability is measured over time.
16. The method of claim 1 further comprising the step of:
providing a line graph of Optical Nerve Sheath Diameter (ONSD) measurements along a length of the 3D modeled segment of the ONS; and
analyzing changes in ONSD, wherein changes in ONSD along a length of the 3D modeled segment of ONS is indicative of brain injury.
17. The method of claim 1 ,
wherein the step of imaging comprises the steps of
obtaining a series of transverse 2D images with an imaging device, and
obtaining position and orientation data of the imaging device relative to the eye for each image as the imaging device is moved across the eye; and
wherein the step of creating said 3D model comprises the step of combining the obtained 2D images with said obtained position and orientation data to create said 3D model.
18. A method for detecting a traumatic brain injury in a subject comprising the steps of:
obtaining a plurality of images of the subject's Optic Nerve Sheath (ONS) along the length of the subject's ONS that extends outwardly behind the eye globe, the images being obtained at a tissue penetration depth of between 3 mm and 25 mm behind the eye globe;
creating a three-dimensional (3D) model of a segments of the subject's ONS from said plurality of images, wherein the 3D model defines at least a volume of the 3D modeled segment;
measuring from the 3D model the volume of the 3D modeled segment to determine a present state of the subject's ONS; and
comparing the subject's present state measured ONS volume to a baseline state 3D model volume in order to detect present distortions of the subject's ONS volume and to thereby characterize a present state of the subject's ONS and a detect a current or recent traumatic brain injury.
19. The method of claim 18 ,
wherein the step of imaging comprises the steps of
obtaining a series of transverse 2D images with an imaging device, and
obtaining position and orientation data of the imaging device relative to the eye for each image as the imaging device is moved across the eye; and
wherein the step of creating said 3D model comprises the step of combining the obtained 2D images with said obtained position and orientation data to create said 3D model.
20. A method for detecting a prior traumatic brain injury in a subject comprising the steps of:
obtaining a plurality of images of the subject's Optic Nerve Sheath (ONS) along the length of the subject's ONS that extends outwardly behind the eye globe, the images being obtained at a tissue penetration depth of between 3 mm and 25 mm behind the eye globe;
creating a three-dimensional (3D) model of a segment of the subjects' ONS from said plurality of images, wherein the 3D model defines at least a surface contour of the 3D modeled segment;
measuring from the 3D model the surface contour of the 3D modeled segment to determine a present state of the subject's ONS; and
comparing the subject's present state measured ONS surface contour to a baseline state 3D model surface contour in order to identify present distortions of the subject's ONS surface contour and to thereby characterize a present state of the subject's ONS and a detect a current or recent traumatic brain injury.
21. The method of claim 20 ,
wherein the step of imaging comprises the steps of
obtaining a series of transverse 2D images with an imaging device, and
obtaining position and orientation data of the imaging device relative to the eye for each image as the imaging device is moved across the eye; and
wherein the step of creating said 3D model comprises the step of combining the obtained 2D images with said obtained position and orientation data to create said 3D model.