System and method for detecting tissue and fiber tract deformation

Patent metadata

Field	Value
Publication number	US-2016005169-A1
Application number	US-201414769507-A
Country	US
Kind code	A1
Filing date	Mar 14, 2014
Priority date	Mar 15, 2013
Publication date	Jan 7, 2016
Grant date	—

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 tissue model of a patient, comprising the steps of:
a) receiving at least one set of input data of tissue of a patient, said at least one set of input data containing directional information being derivable therefrom, of at least one tissue component of the tissue; b) representing the directional information of the at least one component of tissue in a pre-selected format and producing therefrom an oriented tissue map which reflects a directionality of the at least one tissue component; and c) producing a tissue model in which at least one constituent of the tissue model is the oriented tissue map such that the tissue model reflects the directionality of the at least one tissue component so that when the tissue model is subjected to an influence that causes tissue transformation over a period of time, the tissue model evolves over the period of time in a manner which reflects a trajectory of the influence interacting with the directionality of the at least one tissue component.

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 or 2 wherein the tissue model includes further constituents, said further constituents including any one or combination of elasticity properties of one or more tissue components tensile properties of one or more tissue components, pressure properties of one or more tissue components, 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 any one of claims 1 to 3 wherein the pre-selected format used to represent the directional information of the at least one component of the 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 any one of claims 1 to 4 , including visually displaying the tissue model.

6 . The method according to claim 5 , including assigning visual clues to different constituents of the displayed the tissue model.

7 . The method according to any one of claims 1 to 6 wherein the evolvable tissue model is an evolvable tissue model of the patient's brain, and wherein the input data of the at least one tissue component of the tissue is input data of diffusion fiber tracts, and wherein the oriented tissue map is a map showing the white matter fiber tracks.

8 . The method according to claim 7 wherein the 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.

9 . The method according to claim 7 or 8 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.

10 . The method according to claim 7 , 8 or 9 wherein the fluid representations include cerebral spinal fluid, blood, and edema.

11 . The method according to any one of claims 7 to 10 wherein the input data is acquired using any or combination of magnetic resonance diffusion weighted imaging, diffusion tensor imaging, q-ball, HARDI, interferometric approaches including optical coherence tomography, temporal imaging techniques including Doppler ultrasound and functional MRI, algorithmic segmentation and analysis of cellular orientation image data such as microscopy.

12 . The method according to any one of claims 1 to 6 wherein the evolvable tissue model is an evolvable tissue model of the patient's spinal cord, and wherein the input data of the at least one tissue component of the tissue is input data of spinal fiber tracts, and wherein the oriented tissue map is a map showing the spinal fiber tracts.

13 . The method according to claim 12 wherein the 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.

14 . The method according to claim 12 or 13 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.

15 . The method according to claim 12 , 13 or 14 wherein the fluid representations include cerebral spinal fluid, blood, and edema.

16 . The method according to any one of claims 12 to 15 wherein the input data is acquired using any or combination of magnetic resonance diffusion weighted imaging, diffusion tensor imaging, q-ball, HARDI, interferometric approaches including optical coherence tomography, temporal imaging techniques including Doppler ultrasound and functional MRI, algorithmic segmentation and analysis of cellular orientation image data such as microscopy.

17 . A method of modelling effect of an influence on tissue using the evolvable tissue model according to any one of claims 1 to 16 , comprising the steps of;
receiving at least one set of input data of at least one influence to which the 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 tissue model and updating the tissue model after the interaction showing a transformation the tissue model due to the influence, the updated tissue model forming an output.

18 . The method according to claim 17 , including using the updated tissue model, and updated input data of the 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 tissue, and including using the updated 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.

19 . The method according to claim 17 or 18 , wherein the step of interacting the influence model with the tissue model includes iteratively interacting the influence model with the tissue model over a period of time, and including updating the tissue model at selected times during said period of time.

20 .

Assignees

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

• A61B5/0066 Primary

  Optical coherence imaging · CPC title

• G06T7/0012 Primary

  Biomedical image inspection · CPC title

• G06T2207/30012

  Spine; Backbone · CPC title

• A61B34/10

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

• A61B90/11

  with guides for needles or instruments, e.g. arcuate slides or ball joints · CPC title