Methods and apparatus for relative perfusion and/or viability

• US 8,626,263 B2
• Filed: 04/13/2006
• Issued: 01/07/2014
• Est. Priority Date: 04/13/2006
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Hoppel, Bernice Eland, Licato, Paul Edgar, Callister, Tracy Quayle, Broadie, Amy Lynette
• Primary Examiner(s): Lamprecht, Joel
• Application Number:
  US11/403,656
• Publication Number:
  US 20070244389A1
• Time in Patent Office: 2,826 Days
• Field of Search: 600/436, 600/407, 600/410, 600/431, 600/437, 378/4, 378/5, 378/98.9, 378/19
• US Class Current: 600/407
• CPC Class Codes: A61B 6/507 for determination of haemodynamic parameters, e.g. perfusion CT
• CPC Class Codes: A61B 6/508 for non-human patients
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30048 Heart; Cardiac
• CPC Class Codes: G06T 2207/30104 Vascular flow; Blood flow; Perfusion
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

A method of evaluating tissue of an organ includes performing at least one of classification processing and clustering processing to obtain a processed dataset to visualize at least one of the imaging agent, blood, the contrast agent, and the biomedical agent distribution in the tissue, a relative regional uptake of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent in the tissue, relative regional flow of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent and the clearance or persistence of the at least one of the imaging agent, blood, the contrast agent, and the biomedical agent within the tissue and the characterization of elemental components of disease.

Claims

1. A method of evaluating tissue of an organ, said method comprising:

accessing image data from an imaging modality acquisition system of tissue of an organ;

performing a three-dimensional segmentation on the image data to isolate a sub-portion of the image data to allow for more accurate processing;

processing the segmented image data with a statistical classification algorithm;

identifying an abnormality in the tissue of the organ using the processed data;

assigning a degree of severity to the abnormality based on the processed data; and

creating an image visually distinguishing the degree of severity of the abnormality.

2. The method of claim 1 wherein processing the segmented image data comprises performing at least one of a z-scoring of the tissue, a statistical analysis, at least one t-test, a multivariate analysis, a classifier based analysis, an exclusive clustering algorithm, an overlapping and fuzzy clustering algorithm, a partitioning algorithm, a probabilistic clustering, a hierarchical clustering, a K-means analysis, a fuzzy C-means analysis, an expectation maximization analysis, a density based algorithm, a grid-based algorithm, a model based algorithm, and combinations thereof.

3. The method of claim 1 further comprising:

delivering one or two injections to provide classification information of perfusion or viability, wherein the organ is one of a heart, a liver, a brain, a vasculature, and a kidney.

4. The method of claim 1 wherein the abnormality comprises at least one of a volume of ischemic tissue, an area of ischemic tissue infarct, a percentage of ischemic tissue within the organ, an organ wall motion, a relative blood flow to the tissue, a microvasculature density, a microvasculature pattern, at least one calcification, a stroke volume, a mass volume, a percent stenosis, an agent distribution within the organ, an agent clearance throughout the organ, an agent distribution in the tissue, a whole organ uptake of agent, a regional uptake of agent, a regional washout of agent, a regional accumulation of agent, a regional persistence of agent, a regional clearance of agent, a whole organ washout of agent, a clearance in the tissue over a plurality of phases, a thrombosis, a mass shape, and combinations thereof.

5. The method of claim 1 further comprising performing a visualization of the abnormality using a three or four dimensional display of the processed image data represented by at least one of color, texture, and pattern as an overlay of the processed image data.

6. The method of claim 1 further comprising:

comparing the processed image data with one of an originally collected dataset, a curved reformat of the processed dataset, a lumen view of the processed dataset, and combinations thereof; and

performing visualization for preferred presentation to the user including visualization of deficit or hyper intense areas in a three-dimensional display.

7. The method of claim 1 further comprising performing a visualization of the abnormality as a time course in response to physiological motion of the organ or the passage of an imaging agent through the organ over time.

8. The method of claim 1, comprising accessing the image data from one of a single energy CT system and a multi-energy CT system.

9. The method of claim 8 further comprising sending data to one of a PET system, a SPECT system, a MRI system, and an Ultrasound system.

10. The method of claim 1 further comprising performing at least one of reconstructing the image data in an angiographic view for disease evaluation, reformatting the image data for an optimal view or functional characteristics, statistically analyzing a cluster view for disease component analysis, and statistically analyzing the image data for at least one of imaging agent wash-in, clearance, distribution, and accumulation either regionally or globally.

11. The method of claim 1 comprising performing a statistical or a clustering processing to visualize and characterize components of the tissue for evaluation of disease.

12. The method of claim 1 further comprising differentiating between anatomical differences as well as various components of disease.

13. An imaging system comprising:

an acquisition device configured to receive energy regarding an organ; and

a computer coupled to the acquisition device, the computer configured to;

access image data from the acquisition device, wherein the image data comprises a three-dimensional, single or multi-phase set of pixels;

define a plurality of data clusters representing differing ranges of signal intensity;

assign the set of pixels to respective data clusters; and

characterize components of the organ for evaluation of disease based on the pixel assignments.

14. The imaging system of claim 13 wherein the computer is further configured to create an image of the organ wherein the plurality of data clusters are represented by at least one of color, texture, and pattern.

15. The imaging system of claim 14 wherein the computer is further configured to overlay the image as a mask on an underlying grayscale image of the organ.

16. The imaging system of claim 13 wherein the computer is configured to process the image data using at least one of a z-scoring algorithm and a clustering algorithm.

17. A computer readable storage medium having a computer program stored thereon and representing a set of instructions that when executed by a computer cause the computer to:

access an initial image dataset comprising a plurality of pixels corresponding to a tissue;

segment the initial image dataset to isolate a portion of the tissue;

process the segmented image dataset using a classifying algorithm;

identify levels of ischemia corresponding to the plurality of pixels from the processed image dataset; and

create a processed image using the processed image dataset, the processed image having a plurality of visually distinct areas corresponding to the levels of ischemia of the plurality of pixels.

18. The computer readable storage medium of claim 17 wherein the set of instructions cause the computer to process the segmented image dataset using at least one of a z-scoring algorithm and a clustering algorithm.

19. The computer readable storage medium of claim 17 wherein the set of instructions further cause the computer to:

identify a number of pixels of the plurality of pixels corresponding to an infarct; and

determine one of a size, a transmurality, and a volume of the infarct.

20. The computer readable storage medium of claim 17 wherein the set of instructions cause the computer to create a processed image having the plurality of visually distinct areas represented by one of differing colors, textures, and patterns.

21. The computer readable storage medium of claim 20 wherein the set of instructions further cause the computer to:

create an initial image corresponding to the initial data set; and

overlay the processed image as a mask on the initial image.

22. The computer readable storage medium of claim 17 wherein the set of instructions further cause the computer to:

access the initial image dataset of the tissue from an imaging system of a first modality;

access a second image dataset of the tissue from an imaging system of a second modality;

create a secondary image of the tissue from the second image dataset; and

overlay the processed image as a mask on the secondary image.