Method and system for assessment of biomarkers by measurement of response to surgical implant

• US 20060247864A1
• Filed: 04/29/2005
• Published: 11/02/2006
• Est. Priority Date: 04/29/2005
• Status: Abandoned Application

• Current Assignee: Virtual Shopping, Inc.
• Original Assignee: Virtual Shopping, Inc.
• Inventors: Totterman, Saara Marjatta Sofia, Ashton, Edward, Tamez-Pena, Jose
• Application Number:
  US11/117,739
• Publication Number:
  US 20060247864A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

In a human or animal organ or other region of interest, specific objects, such as liver metastases and brain lesions, serve as indicators, or biomarkers, of disease. In a three-dimensional image of the organ, the biomarkers are identified and quantified both before and after a surgical implant is implanted, and their reaction to the surgical implant is observed. 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 at least one biomarker in a region of interest of a patient and a response of the at least one biomarker to a surgical implant, the method comprising:

(a) obtaining at least one pre-implantation 3D image data set in the region of interest before the surgical implant is implanted into the region of interest;

(b) implanting the surgical implant into the region of interest

(c) obtaining at least one post-implantation 3D image data set in the region of interest after the surgical implant has been implanted; and

(d) extracting biomarker measurements from the at least one pre-implantation 3D image data set and the at least one post-implantation 3D image data set.

2. The method of claim 1, wherein steps (a)-(c) result in a plurality of 3D image data sets, and wherein step (d) comprises identifying the at least one biomarker in one of the plurality of 3D image data sets through statistical reasoning

3. The method of claim 2, wherein step (d) further comprises identifying the at least one biomarker in remaining ones of the plurality of 3D image data sets in accordance with an identification of the at least one biomarker identified in said one of the plurality of images through motion tracking and estimation.

4. The method of claim 3, wherein step (d) further comprises forming a 4D model of the region of interest.

5. The method of claim 3, wherein the plurality of 3D image sets are separated in time, and wherein said one of the plurality of 3D image sets is a first one in time of the plurality of 3D image sets.

6. The method of claim 3, wherein the plurality of 3D image sets are separated in time, and wherein said one of the plurality of 3D image sets is a last one in time of the plurality of 3D image sets.

7. The method of claim 3, wherein the plurality of 3D image sets are separated in time, and wherein said one of the plurality of 3D image sets is an intermediate one in time of the plurality of 3D image sets.

8. The method of claim 1, wherein the biomarker measurements comprise at least one higher-order measurement.

9. The method of claim 8, wherein the at least one higher-order measurement comprises a measurement selected from the group consisting of eigenfunction decompositions, moments of inertia, shape analysis, results of morphological operations, fractal analysis, wavelet analysis, advanced surface and shape analysis, and trajectories of bones, joints, tendons, and moving musculoskeletal structures.

10. The method of claim 1, wherein the at least one biomarker comprises a biomarker related to cancer.

11. The method of claim 10, wherein the biomarker related to cancer is selected from the group consisting of:

tumor surface area;

tumor compactness;

tumor surface curvature;

tumor surface roughness;

necrotic core volume;

necrotic core compactness;

necrotic core shape;

viable periphery volume;

volume of tumor vasculature;

change in tumor vasculature over time;

tumor shape;

morphological surface characteristics;

lesion characteristics;

tumor characteristics;

tumor peripheral characteristics;

tumor core characteristics;

bone metastases characteristics;

ascites characteristics;

pleural fluid characteristics;

vessel structure characteristics;

neovasculature characteristics;

polyp characteristics;

nodule characteristics;

angiogenisis characteristics; and

tumor 3D volume.

12. The method of claim 1, wherein the at least one biomarker comprises a biomarker related to joint disease.

13. The method of claim 12, wherein the biomarker related to joint disease 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 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 and number;

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.

14. The method of claim 1, wherein the at least one biomarker comprises a biomarker related to neurological disease.

15. The method of claim 14, wherein the biomarker related to neurological disease is selected from the group consisting of:

a shape, topology, and morphology of brain lesions;

shape, topology, and morphology of brain plaques;

shape, topology, and morphology of brain ischemia;

shape, topology, and morphology of brain tumors;

spatial frequency distribution of sulci and gyri;

compactness of gray matter and white matter;

whole brain characteristics;

gray matter characteristics;

white matter characteristics;

cerebral spinal fluid characteristics;

hippocampus characteristics;

brain sub-structure characteristics;

a ratio of cerebral spinal fluid volume to gray mater and white matter volume; and

a number and volume of brain lesions.

16. The method of claim 1, wherein the at least one biomarker comprises a biomarker related to disease and toxicity in an organ.

17. The method of claim 16, wherein the biomarker related to disease and toxicity is selected from the group consisting of:

organ volume;

organ surface;

organ compactness;

organ shape;

organ surface roughness; and

fat volume and shape.