Voxel-resolution myocardial blood flow analysis

• US 10,321,885 B2
• Filed: 10/03/2014
• Issued: 06/18/2019
• Est. Priority Date: 03/29/2011
• Status: Active Grant

• Current Assignee: Howard University
• Original Assignee: Howard University
• Inventors: Anderson, John M. M.
• Primary Examiner(s): Fujita, Katrina R
• Application Number:
  US15/026,697
• Publication Number:
  US 20160242718A1
• Time in Patent Office: 1,719 Days
• CPC Class Codes: A61B 5/0044 for the heart
• CPC Class Codes: A61B 5/02755 Radioactive tracers
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/507 for determination of haemodynamic parameters, e.g. perfusion CT
• CPC Class Codes: G06T 2207/10104 Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/30104 Vascular flow; Blood flow; Perfusion
• CPC Class Codes: G06T 7/0016 involving temporal comparison
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A myocardial blood flow analysis scan includes incorporating a pharmacological kinetic model with the standard factor analysis model where each time activity curve is assumed to be a linear combination of factor curves. Pharmacological kinetics based factor analysis of dynamic structures (K-FADS-II) model can be applied, whereby estimating factor curves in the myocardium can be physiologically meaningful is provided. Additional optional aspects include performing a discretization to transform continuous-time K-FADS-II model into a discrete-time K-FADS-II model and application of an iterative Improved Voxel-Resolution myocardial blood flow (IV-MBF) algorithm. Where the model is applied without assumption that a right ventricle tissue curve and a left ventricle tissue curve obey a particular mathematical relationship, a least squares objective function can be applied to obtain estimates for parameters of the pharmacological kinetic model by applying a majorize-minimize optimization technique to iteratively estimate the curves.

Claims

1. A method for estimating myocardial blood flow, the method comprising:

a processing device applying a pharmacological kinetic model to a data set stored in a storage device, the data set derived from an imaging technique based on monitoring fluid based tracers in a left ventricle, a right ventricle, and myocardium, wherein the pharmacological kinetic model includes;

incorporating a three-compartment kinetic model of changing concentrations of bound fluid based tracers, unbound fluid based tracers, and blood plasma fluid based tracers into a standard factor analysis of dynamic structures model without assumption that a right ventricle tissue curve and a left ventricle tissue curve obey a particular mathematical relationship by discretizing a continuous time model of the changing concentrations of the bound fluid based tracers, the unbound fluid based tracers, and the blood plasma into a discrete time model using non-negative iterate values for the right ventricle tissue curve and the left ventricle tissue curve and non-negative factor weights, and;

the processing device outputting a processed data set based on the application of the pharmacological kinetic model to the data set for providing a representation of blood flow in the myocardium.

2. The method of claim 1 further comprising the processing device applying a least squares objective function to obtain estimates for parameters of the pharmacological kinetic model.

3. The method of claim 2 further comprising the processing device minimizing the least squares objective function.

4. The method of claim 3 further comprising the processing device minimizing the least squares objective function by applying a majorize-minimize optimization technique to iteratively estimate the right ventricle tissue curve and the left ventricle tissue curve in combination with estimating the blood flow in the myocardium, wherein estimating the blood flow in the myocardium using iteratively updated estimates of the right ventricle tissue curve and the left ventricle tissue curve.

5. The method of claim 1 further comprising the processing device smoothing estimates for blood flow for a given voxel by applying a limiting factor or penalty factor on data from voxels located within a given distance from the given voxel.

6. The method of claim 1 further comprising the processing device determining initial estimates for an initial estimated right ventricle time activity curve and an initial estimated left ventricle time activity curve and using the initial estimates for the initial estimate right ventricle time activity curve and the initial estimated left ventricle time activity curve to determine initial parameters of the pharmacological kinetic model.

7. The method of claim 1 wherein the pharmacological kinetic model includes a semi-parametric model configured to drive time activity curves for the right ventricle imaging activity from the fluid based tracers and the left ventricle imaging activity from the fluid based tracers to zero over time.

8. A non-transitory computer readable medium storing instructions that cause a processing device, in response to executing the instructions, to perform operations comprising:

the processing device applying a pharmacological kinetic model to a data set stored in a storage device, the data set derived from an imaging technique based on monitoring fluid based tracers in a left ventricle, a right ventricle, and myocardium, wherein the pharmacological kinetic model includes;

incorporating a three-compartment kinetic model of changing concentrations of bound fluid based tracers, unbound fluid based tracers, and blood plasma fluid based tracers into a standard factor analysis of dynamic structures model without assumption that a right ventricle tissue curve and a left ventricle tissue curve obey a particular mathematical relationship by discretizing a continuous time model of the changing concentrations of the bound fluid based tracers, the unbound fluid based tracers, and the blood plasma into a discrete time model using non-negative iterate values for the right ventricle tissue curve and the left ventricle tissue curve and non-negative factor weights, and;

the processing device outputting a processed data set based on the application of the pharmacological kinetic model to the data set for providing a representation of blood flow in the myocardium.

9. The non-transitory computer readable medium of claim 8 further comprising instructions to cause the processing device to perform operations comprising applying a least squares objective function to obtain estimates for parameters of the pharmacological kinetic model.

10. The non-transitory computer readable medium of claim 9 further comprising instructions to cause the processing device to perform operations comprising minimizing the least squares objective function.

11. The non-transitory computer readable medium of claim 10 further comprising instructions to cause the processing device to perform operations comprising minimizing the least squares objective function by applying a majorize-minimize optimization technique to iteratively estimate the right ventricle tissue curve and the left ventricle tissue curve in combination with estimating the blood flow in the myocardium, wherein estimating the blood flow in the myocardium using iteratively updated estimates of the right ventricle tissue curve and the left ventricle tissue curve.

12. The non-transitory computer readable medium of claim 8 further comprising instructions to cause the processing device to perform operations comprising smoothing estimates for blood flow for a given voxel by applying a limiting factor or penalty factor on data from voxels located within a given distance from the given voxel.

13. The non-transitory computer readable medium of claim 8 further comprising instructions to cause the processing device to perform operations comprising determining initial estimates for an initial estimated right ventricle time activity curve and an initial estimated left ventricle time activity curve and using the initial estimates for the initial estimate right ventricle time activity curve and the initial estimated left ventricle time activity curve to determine initial parameters of the pharmacological kinetic model.

14. The non-transitory computer readable medium of claim 8 wherein the pharmacological kinetic model includes a semi-parametric model configured to drive time activity curves for the right ventricle imaging activity from the fluid based tracers and the left ventricle imaging activity from the fluid based tracers to zero over time.

15. An apparatus comprising:

a processing device configured to apply a pharmacological kinetic model to a data set stored in a storage device, the data set derived from an imaging technique based on monitoring fluid based tracers in a left ventricle, a right ventricle, and myocardium, wherein the pharmacological kinetic model includes;

incorporating a three-compartment kinetic model of changing concentrations of bound fluid based tracers, unbound fluid based tracers, and blood plasma fluid based tracers into a standard factor analysis of dynamic structures model without assumption that a right ventricle tissue curve and a left ventricle tissue curve obey a particular mathematical relationship by discretizing a continuous time model of the changing concentrations of the bound fluid based tracers, the unbound fluid based tracers, and the blood plasma into a discrete time model using non-negative iterate values for the right ventricle tissue curve and the left ventricle tissue curve and non-negative factor weights, and;

the processing device configured to output a processed data set based on the application of the pharmacological kinetic model to the data set for providing a representation of blood flow in the myocardium.

16. The apparatus of claim 15 wherein the processing device is configured to apply a least squares objective function to obtain estimates for parameters of the pharmacological kinetic model.

17. The apparatus of claim 16 wherein the processing device is configured to minimize the least squares objective function by applying a majorize-minimize optimization technique to iteratively estimate the right ventricle tissue curve and the left ventricle tissue curve in combination with estimating the blood flow in the myocardium, wherein estimating the blood flow in the myocardium using iteratively updated estimates of the right ventricle tissue curve and the left ventricle tissue curve.

18. The apparatus of claim 15 wherein the processing device is configured to smooth estimates for blood flow for a given voxel by applying a limiting factor or penalty factor on data from voxels located within a given distance from the given voxel.

19. The apparatus of claim 15 wherein the processing device is further configured to determine initial estimates for an initial estimated right ventricle time activity curve and an initial estimated left ventricle time activity curve and to use the initial estimates for the initial estimate right ventricle time activity curve and the initial estimated left ventricle time activity curve to determine initial parameters of the pharmacological kinetic model.

20. The apparatus of claim 15 wherein the pharmacological kinetic model includes a semi-parametric model configured to drive time activity curves for the right ventricle imaging activity from the fluid based tracers and the left ventricle imaging activity from the fluid based tracers to zero over time.