System and method for quantitative assessment of joint diseases and the change over time of joint diseases

• US 20030035773A1
• Filed: 07/26/2002
• Published: 02/20/2003
• Est. Priority Date: 07/27/2001
• Status: Abandoned Application

• Current Assignee: VirtualScopics, LLC (Koninklijke Philips N.V.)
• Original Assignee: VirtualScopics, LLC (Koninklijke Philips N.V.)
• Inventors: Parker, Kevin J., Ashton, Edward, Tamez-Pena, Jose, Sofia Totterman, Saara Marjatta
• Application Number:
  US10/205,606
• Publication Number:
  US 20030035773A1
• Time in Patent Office: Days
• US Class Current: 424/9.1
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/0016 involving temporal comparison
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51

Abstract

In a human or animal joint, specific objects serve as indicators, or biomarkers, of joint disease. In a three-dimensional image of the joint, the biomarkers are identified and quantified. Multiple three-dimensional images can be taken over time, in which the biomarkers can be tracked over time. Statistical segmentation techniques are used to identify the biomarker in a first image and to carry the identification over to the remaining images.

Claims

1. A method for assessing a joint of a patient, the method comprising:

(a) taking at least one three-dimensional image of the joint;

(b) identifying at least one biomarker in the at least one three-dimensional image;

(c) deriving at least one quantitative measurement of the at least one biomarkers; and

(d) storing an identification of the at least one biomarker and the at least one quantitative measurement in a storage medium.

2. The method of claim 1, wherein step (d) comprises storing the at least one three-dimensional image in the storage medium.

3. The method of claim 1, wherein step (b) comprises statistical segmentation of the at least one three-dimensional image to identify the at least one biomarker.

4. The method of claim 1, wherein the at least one three-dimensional image comprises a plurality of three-dimensional images of the joint taken over time.

5. The method of claim 4, wherein step (b) comprises statistical segmentation of a three-dimensional image selected from the plurality of three-dimensional images to identify the at least one biomarker.

6. The method of claim 5, wherein step (b) further comprises motion tracking and estimation to identify the at least one biomarker in the plurality of three-dimensional images in accordance with the at least one biomarker identified in the selected three-dimensional image.

7. The method of claim 6, wherein the plurality of three-dimensional images and the at least one biomarker identified in the plurality of three-dimensional images are used to form a model of the joint and the at least one biomarker in three dimensions of space and one dimension of time.

8. The method of claim 7, wherein the biomarker is tracked over time in the model.

9. The method of claim 1, wherein a resolution in all three dimensions of the at least one three-dimensional image is finer than 1 mm.

10. The method of claim 9, wherein the at least one quantitative measurement comprises a higher order quantitative measurement.

11. The method of claim 10, wherein the higher order quantitative measurement comprises at least one of curvature, topology and shape.

12. The method of claim 1, wherein the at least one biomarker is selected from the group consisting of:

shape of a subchondral bone plate;

layers of cartilage and their relative size;

signal intensity distribution within the cartilage layers;

contact area between articulating cartilage surfaces;

surface topology of a cartilage shape;

intensity of bone marrow edema;

separation distances between bones;

meniscus shape;

meniscus surface area;

meniscus contact area with cartilage;

cartilage structural characteristics;

cartilage surface characteristics;

meniscus structural characteristics;

meniscus surface characteristics;

pannus structural characteristics;

joint fluid characteristics;

osteophyte characteristics;

bone characteristics;

lytic lesion characteristics;

prosthesis contact characteristics;

prosthesis wear;

joint spacing characteristics;

tibia medial cartilage volume;

tibia lateral cartilage volume;

femur cartilage volume;

patella cartilage volume;

tibia medial cartilage curvature;

tibia lateral cartilage curvature;

femur cartilage curvature;

patella cartilage curvature;

cartilage bending energy;

subchondral bone plate curvature;

subchondral bone plate bending energy;

meniscus volume;

osteophyte volume;

cartilage T2 lesion volumes;

bone marrow edema volume and number;

synovial fluid volume;

synovial thickening;

subchondrial bone cyst volume;

kinematic tibial translation;

kinematic tibial rotation;

kinematic tibial valcus;

distance between vertebral bodies;

degree of subsidence of cage;

degree of lordosis by angle measurement;

degree of off-set between vertebral bodies;

femoral bone characteristics; and

patella characteristics.

13. The method of claim 1, wherein step (a) is performed through magnetic resonance imaging.

14. A system for assessing a joint of a patient, the system comprising:

(a) an input device for receiving at least one three-dimensional image of the joint;

(b) a processor, in communication with the input device, for receiving the at least one three-dimensional image of the joint, identifying at least one biomarker in the at least one three-dimensional image and deriving at least one quantitative measurement of the at least one biomarker;

(c) storage, in communication with the processor, for storing the at least one three-dimensional image, an identification of the at least one biomarker and the at least one quantitative measurement; and

(d) an output device for displaying the at least one three-dimensional image, the identification of the at least one biomarker and the at least one quantitative measurement.

15. The system of claim 14, wherein the storage also stores the at least one three-dimensional image.

16. The system of claim 14, wherein the processor identifies the at least one biomarker through statistical segmentation of the at least one three-dimensional image.

17. The system of claim 14, wherein the at least one three-dimensional image comprises a plurality of three-dimensional images of the joint taken over time.

18. The system of claim 17, wherein the processor identifies the at least one biomarkers through statistical segmentation of a three-dimensional image selected from the plurality of three-dimensional images.

19. The system of claim 18, wherein the processor uses motion tracking and estimation to identify the at least one biomarker in the plurality of three-dimensional images in accordance with the at least one biomarker identified in the selected three-dimensional image.

20. The system of claim 19, wherein the plurality of three-dimensional images and the at least one biomarker identified in the plurality of three-dimensional images are used to form a model of the joint and the at least one biomarker in three dimensions of space and one dimension of time.

21. The system of claim 14, wherein a resolution in all three dimensions of the at least one three-dimensional image is finer than 1 mm.

22. The system of claim 14, wherein the at least one quantitative measurement comprises a higher order quantitative measurement.

23. The system of claim 22, wherein the higher order quantitative measurement comprises at least one of curvature, topology and shape.

24. The system of claim 14, wherein the at least one biomarker is selected from the group consisting of:

shape of a subchondral bone plate;

layers of cartilage and their relative size;

signal intensity distribution within the cartilage layers;

contact area between articulating cartilage surfaces;

surface topology of a cartilage shape;

intensity of bone marrow edema;

separation distances between bones;

meniscus shape;

meniscus surface area;

meniscus contact area with cartilage;

cartilage structural characteristics;

cartilage surface characteristics;

meniscus structural characteristics;

meniscus surface characteristics;

pannus structural characteristics;

joint fluid characteristics;

osteophyte characteristics;

bone characteristics;

lytic lesion characteristics;

prosthesis contact characteristics;

prosthesis wear;

joint spacing characteristics;

tibia medial cartilage volume;

tibia lateral cartilage volume;

femur cartilage volume;

patella cartilage volume;

tibia medial cartilage curvature;

tibia lateral cartilage curvature;

femur cartilage curvature;

patella cartilage curvature;

cartilage bending energy;

subchondral bone plate curvature;

subchondral bone plate bending energy;

meniscus volume;

osteophyte volume;

cartilage T2 lesion volumes;

bone marrow edema volume and number;

synovial fluid volume;

synovial thickening;

subchondrial bone cyst volume;

kinematic tibial translation;

kinematic tibial rotation;

kinematic tibial valcus;

distance between vertebral bodies;

degree of subsidence of cage;

degree of lordosis by angle measurement;

degree of off-set between vertebral bodies;

femoral bone characteristics; and

patella characteristics.