System and method for detecting tissue and fiber tract deformation

Patent metadata

Field	Value
Publication number	US-9922417-B2
Application number	US-201414769507-A
Country	US
Kind code	B2
Filing date	Mar 14, 2014
Priority date	Mar 15, 2013
Publication date	Mar 20, 2018
Grant date	Mar 20, 2018

Abstract

Disclosed herein is a method for producing an evolvable tissue model of a patient and, using this model, modelling physical transformations of the tissue (e.g. deformation) of the tissue model by interacting the tissue model with influence models which model interactions with the tissue such as surgical instruments, pressure, swelling, temperature changes etc. The model is produced from a set of input data of the tissue which includes directional information of the tissue. The directional information is used to produce an oriented tissue map. A tissue model is then produced from the oriented tissue map such that the tissue model reflects the directionality of the tissue component. When the tissue model is subjected to an influence that causes tissue deformation over a period of time, the tissue model directionally deforms over the period of time in a manner which reflects a trajectory of the influence interacting with the directionality of the tissue component.

First claim

Therefore what is claimed is:

1. A method for producing an evolvable cerebrospinal tissue model of a patient, comprising the steps of:
a) receiving at least one set of diffusion imaging input data of cerebrospinal tissue of a patient, said at least one set of input data containing at least directional information with respect to diffusion fiber tracts;
b) representing the directional information of the at least one component of cerebrospinal tissue in a pre-selected format and producing therefrom an oriented fiber tract tissue map; and
c) producing a cerebrospinal tissue model in which at least one constituent of the cerebrospinal tissue model uses the oriented diffusion fiber tract tissue map such that the cerebrospinal tissue model reflects the directionality of the diffusion fiber tracts so that when the cerebrospinal tissue model is subjected to an influence any evolution of the cerebrospinal tissue model over time incorporates the directionality of the diffusion fiber tracts, and wherein at least one additional constituent of the cerebrospinal tissue model uses the oriented diffusion fiber tract tissue map such that the cerebrospinal tissue model represents any one of elasticity properties of the one or more tissue components, tensile properties of one or more tissue components or pressure properties of one or more tissue components as a geometric model.

2. The method according to claim 1 wherein step b) is executed using the directional information as acquired when said directional information is usable in a format as received in step a), and wherein in the event the directional information is not usable as is, including a step of preprocessing the input data and extracting therefrom directional information of the at least one component of the tissue in a usable format.

3. The method according to claim 1 wherein the cerebrospinal tissue model includes further constituents, said further constituents including any one or combination of the segmentations of various tissue type(s) relative to any of the input data sets and associated boundaries between tissue types, vasculature, fluid representations, skeletal or musculoskeletal representations, skin and/or other organs as geometric models.

4. The method according to claim 1 wherein the preselected format used to represent the directional information of the at least one component of the cerebrospinal tissue includes an image format, and/or a geometric model, and/or a scalar field, and/or a vector field, and/or a tensor field, and/or a representation of angular components such as via quaternion or rotation matrices, and/or a decomposition of angular components via any appropriate basis such as spherical harmonics, and/or any generalized functional representation of directional orientation.

5. The method according to claim 1 , including visually displaying the cerebrospinal tissue model.

6. The method according to claim 1 wherein the cerebrospinal tissue model of the patient's brain includes further constituents, said further constituents including any one or combination of physical or biomechanical properties of any one or combination of constituents of the brain and head.

7. The method according to claim 3 wherein the boundaries between tissue types includes boundaries between any combination of grey matter, white matter, cerebral spinal fluid, sulcal locations, tumor, bone, meninges, vasculature, and ventricles.

8. The method according to claim 3 wherein the fluid representations include cerebral spinal fluid, blood, and edema.

9. The method according to claim 1 wherein the diffusion imaging input data is acquired using any one or combination of magnetic resonance diffusion weighted imaging techniques such as diffusion tensor imaging, q-ball, and HARDI; and interferometric approaches including optical coherence tomography, and algorithmic segmentation.

10. The method according to claim 1 wherein the cerebrospinal tissue model of the patient's spinal cord includes further constituents, said further constituents including any one or combination of physical or biomechanical properties of any one or combination of spinal meninges, cerebral spinal fluid, vasculature, tumor, bone including vertebrae, intervertebral fibrocartilage, muscle, tendon, cartilage, or ligament.

11. The method according to claim 3 wherein the boundaries between tissue types includes boundaries between any combination of cerebral spinal fluid, meninges tumor, bone including vertebrae, intervertebral fibrocartilage, vasculature, muscle, tendon, cartilage, and ligament.

12. The method according to claim 11 wherein the fluid representations include cerebral spinal fluid, blood, and edema.

13. The method according to claim 1 wherein the diffusion imaging input data is acquired using any one or combination of magnetic resonance diffusion weighted imaging techniques such as diffusion tensor imaging, q-ball, and HARDI; and interferometric approaches including optical coherence tomography, and algorithmic segmentation.

14. A method of modelling effect of an influence on tissue using the evolvable cerebrospinal tissue model according to claim 1 , comprising the steps of;
receiving at least one set of input data of at least one influence to which the diffusion imaging tissue is to be subjected;
preprocessing the at least one set of input data and extracting therefrom parameters of said influence;
representing the parameters of said influence in a pre-selected format; and
producing at least one influence model from the represented parameters of the influence; and
interacting the influence model with the cerebrospinal tissue model and updating the cerebrospinal tissue model after the interaction of the influence with the oriented diffusion fiber tract tissue map showing a transformation of the cerebrospinal tissue model due to the influence, the updated cerebrospinal tissue model forming an output.

15. The method according to claim 14 , including using the updated cerebrospinal tissue model, and updated input data of the cerebrospinal tissue of the patient in the step of preprocessing the input data and extracting therefrom updated directional information of the at least one component of the diffusion imaging tissue, and including using the updated diffusion imaging tissue model, and updated input data of said at least one influence in the step of preprocessing the at least one set of input data and extracting therefrom updated parameters of said influence.

16. The method according to claim 14 , wherein the step of interacting the influence model with the cerebrospinal tissue model includes iteratively interacting the influence model with the tissue model over a period of time, and including updating the diffusion imaging tissue model at selected times during said period of time.

17. The method according to claim 14 including visually displaying the updated cerebrospinal tissue model and/or storing the updated cerebrospinal tissue model.

18. The method according to claim 14 , wherein the influence is a surgical instrument inserted into the tissue, and wherein the influence model includes at least dimensions and a shape of the instrument penetrating the cerebrospinal tissue, and wherein the method of modelling the effect of an influence includes an iterative process which includes movement of the instrument a given distance in a given period of time, with the output showing an amount of cerebrospinal tissue deformation at a selected time.

19. A system for producing an evolvable cerebrospinal tissue model of a patient, comprisin

Assignees

• Sela Gal
• Alexander Simon
• Piron Cameron
• Richmond Joshua
• Wood Michael
• Yuwaraj Murugathas
• Gallop David
• Hodges Wes
• Thomas Monroe M
• Synaptive Medical Barbados Inc

Inventors

• Sela Gal
• Alexander Simon
• Piron Cameron
• Richmond Joshua
• Wood Michael
• Yuwaraj Murugathas
• Gallop David
• Hodges Wes
• Thomas Monroe M

Classifications

• A61B34/10

  Computer-aided planning, simulation or modelling of surgical operations · CPC title

• A61B5/055

  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging · CPC title

• A61B2034/105

  Modelling of the patient, e.g. for ligaments or bones · CPC title

• G06T2207/30012

  Spine; Backbone · CPC title

• G06T7/0012 Primary

  Biomedical image inspection · CPC title