Factor analysis in medical imaging

• US 7,519,211 B2
• Filed: 09/19/2006
• Issued: 04/14/2009
• Est. Priority Date: 10/15/2004
• Status: Active Grant

• Current Assignee: Beth Israel Deaconess Medical Center, Inc. (Beth Israel Lahey Health, Inc.)
• Original Assignee: Beth Israel Deaconess Medical Center, Inc. (Beth Israel Lahey Health, Inc.), Brigham and Women's Hospital Incorporated
• Inventors: El Fakhri, Georges, Sitek, Arkadiusz
• Primary Examiner(s): Alavi; Amir
• Application Number:
  US11/533,145
• Publication Number:
  US 20070014463A1
• Time in Patent Office: 938 Days
• Field of Search: 382/103, 382/154, 382128-134, 382/164, 382/173, 382/190, 382/272, 382/276, 382/285, 382/262, 382/172, 250/394, 378/4, 378 8- 9, 378/901, 128/623.1, 434/262, 434/272, 434/323, 600/431, 600/437, 600/483, 600/509
• US Class Current: 382/128
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/503 for diagnosis of the heart
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: G06T 2207/30024 Cell structures in vitro; Tissue sections in vitro
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A method for processing image data includes estimating initial factor images from image data, transforming the estimated initial factor images by a transformation variable to obtain transformed factor images, providing an objective function that is a function of the transformed factor images, and minimizing the objective function to obtain unique factor images from the estimated initial factor images.

Claims

1. A method for estimating kinetic parameters from image data, the method comprising:

using a processor for executing instructions for,

providing a model of kinetic contributions from first and second physiological regions;

grouping voxels of the image data into first and second groups;

determining an average value of the factors associated with the first group, the factors corresponding to blood-flow time activity curves;

incorporating the average value into the model;

estimating the kinetic parameters based on the model;

determining a first seed voxel for which a sum of distances between the first seed voxel and other voxels of the image data is greatest; and

selecting, from ungrouped voxels, a first predefined number of ungrouped voxels that are located nearest to the first seed voxel; and

wherein grouping voxels of the image data into the first group comprises defining a first group that includes the selected ungrouped voxels and the first seed voxel.

2. The method of claim 1, further comprising providing input functions to the model, the input functions comprising factors determined for the first and second physiological regions.

3. The method of claim 1, further comprising reducing a vector space spanned by the voxels to a subspace within the vector space, the subspace being defined by principal vectors of the voxels.

4. The method of claim 1, further comprising:

determining a second seed voxel for which a sum of distances between the second seed voxel and other voxels of the image data is greatest, the other voxels being remaining ungrouped voxels;

selecting, from the remaining ungrouped voxels, a second predefined number of ungrouped voxels that are located nearest to the second seed voxel; and

wherein grouping voxels of the image data into the second group comprises defining a second group that includes the selected remaining ungrouped voxels and the second seed voxel.

5. The method of claim 4, further comprising selecting the first predefined number to be equal to the second predefined number.

6. The method of claim 1, further comprising:

determining ungrouped voxels located within a minimum distance from the first seed voxel; and

wherein grouping voxels of the image data into the first group comprises grouping the ungrouped voxels and the first seed voxel.

7. The method of claim 1, wherein

providing the model comprises selecting the model to be a two-compartment model of myocardial factors and of kinetic contributions from a ventricle and a right ventricle;

and

wherein estimating the kinetic parameters comprises determining extraction and egress rates of transport between myocardial tissue and freely diffusible space.

8. A method for estimating kinetic parameters from image data, the method comprising:

using a processor for executing instruction for,

providing a model of kinetic contributions from first and second physiological regions;

determining a first seed voxel for which a sum of distances between the first seed voxel and other voxels of the image data is greatest;

selecting, from ungrouped voxels, a first predefined number of ungrouped voxels that are located nearest to the first seed voxel;

grouping voxels of the image data into first and second groups, wherein the first group includes the selected ungrouped voxels and the first seed voxel;

determining an average value of the factors associated with the first group;

incorporating the average value into the model; and

estimating the kinetic parameters based on the model.

9. The method of claim 8, further comprising:

determining a second seed voxel for which a sum of distances between the second seed voxel and other voxels of the image data is greatest, the other voxels being remaining ungrouped voxels;

selecting, from the remaining ungrouped voxels, a second predefined number of ungrouped voxels that are located nearest to the second seed voxel; and

wherein grouping voxels of the image data into the second group comprises defining a second group that includes the selected remaining ungrouped voxels and the second seed voxel.

10. The method of claim 9, further comprising selecting the first predefined number to be equal to the second predefined number.

11. A method for estimating kinetic parameters from image data, the method comprising:

using a processor for executing instructions for,

providing a model of kinetic contributions from first and second physiological regions;

determining a first seed voxel for which a sum of distances between the first seed voxel and other voxels of the image data is greatest;

determining ungrouped voxels located within a minimum distance from the first seed voxel;

grouping voxels of the image data into the first group and second groups, wherein the first group includes the selected ungrouped voxels and the first seed voxel;

determining an average value of the factors associated with the first group;

incorporating the average value into the model; and

estimating the kinetic parameters based on the model.

12. The method for estimating kinetic parameters from image data, the method comprising:

using a processor for executing instructions for,

providing a model of kinetic contributions from first and second physiological regions, wherein providing the model includes selecting the model to be a two-compartment model of myocardial factors and of kinetic contributions from a left ventricle and a right ventricle;

grouping voxels of the image data into first and second groups;

determining an average value of the factors associated with the first group;

incorporating the average value into the model; and

estimating the kinetic parameters based on the model, wherein estimating the kinetic parameters includes determining extraction and egress rates of transport between myocardial tissue and freely diffusible space.