Joint and Cartilage Diagnosis, Assessment and Modeling

• US 20090076371A1
• Filed: 11/25/2008
• Published: 03/19/2009
• Est. Priority Date: 09/14/1998
• Status: Abandoned Application

• Current Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford Management Co.)
• Original Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford Management Co.)
• Inventors: Lang, Philipp, Steines, Daniel, Andriacchi, Thomas P., Alexander, Eugene J.
• Application Number:
  US12/277,809
• Publication Number:
  US 20090076371A1
• Time in Patent Office: Days
• US Class Current: 600/407
• CPC Class Codes: A61B 2034/105 Modelling of the patient, e.g. for ligaments or bones
• CPC Class Codes: A61B 2576/02 specially adapted for a particular organ or body part
• CPC Class Codes: A61B 5/0036 including treatment, e.g., using an implantable medical device, ablating, ventilating
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/1038 Measuring plantar pressure during gait
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/1078 by moulding
• CPC Class Codes: A61B 5/4514 Cartilage
• CPC Class Codes: A61B 5/4528 Joints A61B5/4533, A61B5/4538 take precedence
• CPC Class Codes: A61B 5/4585 Evaluating the knee
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: G01R 33/50 based on the determination of relaxation times , e.g. T1 measurement by IR sequences; T2 measurement by multiple-echo sequences
• CPC Class Codes: G01R 33/56 Image enhancement or correction, e.g. subtraction or averaging techniques , e.g. improvement of signal-to-noise ratio and resolution
• CPC Class Codes: G01R 33/5608 Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels image data processing or generation, in general G06T
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/30008 Bone
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/187 involving region growing; involving region merging; involving connected component labelling

Abstract

Methods for assessing the condition of a cartilage in a joint and assessing cartilage loss, particularly in a human knee, are disclosed. In one method, a method for modeling healthy cartilage, the method includes measuring the distance between each of at least two points on an articular cartilage surface corresponding to margins of a defect and a corresponding point on the sub-chondral bone surface, and interpolating a reconstructed cartilage boundary. In a second method, a method for generating a three-dimensional approximation of healthy cartilage, the method includes determining a set of distances between at least three points on a first articular surface corresponding to margins of a defect and at least three corresponding points on a second cartilage surface, and approximating a reconstructed cartilage surface.

Claims

1. A method of modeling healthy cartilage comprising:

a. obtaining image data representing at least a portion of a defect in cartilage to be assessed;

b. determining from the image data the location of a articular cartilage surface having the defect;

c. determining from the image data the location of a subchondral-bone surface;

d. locating at least two points on the articular cartilage surface approximately corresponding to margins of the defect;

e. measuring the distance between each of the at least two points on the articular cartilage surface and a corresponding point on the subchondral-bone surface; and

f. interpolating, based on the measured distances, a reconstructed cartilage boundary that provides an approximation of the location of the first cartilage surface without the defect in the cartilage.

2. The method of claim 1, wherein the reconstructed cartilage surface is based on a two-dimensional image.

3. The method of claim 1, wherein the second cartilage boundary does not contain a defect.

4. The method of claim 1, wherein the reconstructed cartilage boundary is a three-dimensional surface.

6. The method of claim 1, wherein the reconstructed cartilage surface is a first reconstructed cartilage boundary corresponding to a first cross-sectional view of the joint and further comprising:

a. generating a second reconstructed cartilage boundary corresponding to a second cross-sectional view of the joint; and

b. creating a three-dimensional reconstructed cartilage surface based on the first and second reconstructed cartilage boundary.

5. The method of claim 5, wherein the reconstructed cartilage boundary is approximated at least in part by summing a set of slices of the image data.

7. A method of generating a three-dimensional approximation of healthy cartilage comprising:

a. obtaining three-dimensional image data corresponding to a joint having defective cartilage;

b. determining from the image data the location of a first cartilage surface having a defect;

c. determining from the image data the location of a second cartilage surface;

d. locating at least three points on the first cartilage surface approximately corresponding to margins of the defect;

e. determining a set of distances between each of the at least three points on the first cartilage surface and at least three corresponding points on the second cartilage surface; and

f. approximating, based on the set of distances, a reconstructed cartilage surface that represents the first cartilage surface without the defect in the cartilage.

8. The method of claim 7, wherein the step of determining a set of distances further comprises transforming the image data using, at least in part, a three-dimensional Euclidian distance transform.

9. A method of assessing a defect in cartilage in a joint comprising:

a. obtaining image data representing a defect to be assessed;

b. determining from the image data the location of a defective cartilage surface;

c. locating at least two points on the defective cartilage surface approximately corresponding to margins of the defect;

d. creating a current data set based at least in part on the at least two points on the defective cartilage surface; and

e. approximating a characteristic of the defect, based at least in part on the data set.

10. The method of claim 9, wherein the approximated characteristic is the area of the defect.

11. The method of claim 9, wherein the approximated characteristic is the volume of the defect.

12. The method of claim 9, wherein the approximated characteristic is the depth of the defect.

13. The method of claim 9, further comprising comparing the current data set to a previous data set to determine changes in the joint.

14. The method of claim 13, wherein the comparison is made without registering the current data set to the pervious data set.

15. The method of claim 9, further comprising:

a. determining from the image data the location of a second cartilage surface; and

b. determining a set of distances between each of the at least two points on the defective cartilage surface and corresponding points on the second cartilage surface;

wherein the data set is based at least in part on the set of distances.