Fast analysis method for non-invasive imaging of blood flow using vessel-encoded arterial spin labelling

Patent metadata

Field	Value
Publication number	US-9757047-B2
Application number	US-201313815815-A
Country	US
Kind code	B2
Filing date	Mar 14, 2013
Priority date	Mar 20, 2012
Publication date	Sep 12, 2017
Grant date	Sep 12, 2017

Abstract

Arterial spin labelling (ASL) MRI offers a non-invasive means to create blood-borne contrast in vivo for dynamic angiographic imaging. By spatial modulation of the ASL process it is possible to uniquely label individual arteries over a series of measurements, allowing each to be separately identified in the resulting images. This separation requires appropriate analysis for which a general framework has previously been proposed. Here the general framework is modified for fast analysis of non-invasive imaging of blood flow using vessel encoded arterial spin labelling (VE-ASL). This specifically addresses the issues of computational speed of the analysis and the robustness required to deal with real patient data. The modification applies various approaches for estimation of one or more parameters that change the way a vessel, for example an artery, is encoded to provide the fast analysis.

First claim

What is claimed:

1. A method for analysis of vessel-encoded arterial spin labelling (VE-ASL) data, comprising:
A. Obtaining data for a region of interest in a subject;
B. Estimating, in view of the data obtained, one or more parameters that describe an encoding within a labelling region of one or more vessels that supply the region of interest in the subject;
C. Acquiring vessel-encoded arterial spin labelling (VE-ASL) data for the one or more vessels in the region of interest; and
D. Applying the estimation of the one or more parameters for the analysis of the VE-ASL data to provide an image of blood flow in the region of interest that identifies the individual contributions from the vessels in the labelling region.

2. The method of claim 1 , further comprising determining from the blood flow of the one or more vessels within the region of interest an estimate of the flow contributions from those vessels to a selected voxel of interest.

3. The method of claim 1 , wherein the one or more parameters includes one or more vessel locations.

4. The method of claim 1 , wherein the one or more parameters further includes one or more of, speed of flow, off resonance effects or vessel class proportions.

5. The method of claim 1 , wherein the data is obtained as a part of a planning process acquisition and the one or more parameters are derived from data from the planning process acquisition to provide an estimate of the vessel locations within the labelling region.

6. The method of claim 5 , wherein the one or more parameters represent a relationship between the true vessel locations in the region of interest and the vessel locations estimated from the planning process acquisition.

7. The method of claim 1 , wherein the estimation is derived from a marginalized a posteriori distribution of the VE-ASL data within the region of interest to derive point estimates of the location of the one or more vessels.

8. The method of claim 7 , wherein the estimation is derived from a maximum a posteriori (MAP) optimization.

9. The method of claim 1 , wherein the analysis is applicable to all types of vessel encoded arterial spin labelling (VE-ASL) data acquisitions.

10. The method of claim 1 , further including the steps of providing a magnetic resonance imaging (MRI) device, positioning the subject in association with the MRI device, and using the MRI device to obtain the vessel-encoded blood flow data and acquire the vessel-encoded arterial spin labelling (VE-ASL) data.

11. A system, comprising
at least one computing device;

at least one application executable in the at least one computing device, the at least one application comprising logic that:
A. obtains data for a region of interest in a subject;
B. estimates, in view of the data obtained, one or more parameters that describe an encoding within a labelling region of one or more vessels that supply the region of interest in the subject;
C. acquires vessel-encoded arterial spin labelling (VE-ASL) data for the one or more vessels in the region of interest; and
D. applies the estimation of the one or more parameters for the analysis of the VE-ASL data to provide an image of blood flow in the region of interest that identifies the individual contributions from the vessels in the labelling region.

12. The system of claim 11 , further comprising logic that determines from the blood flow of the one or more vessels within the region of interest an estimate of the flow contributions from those vessels to a selected voxel of interest.

13. The system of claim 11 , wherein the one or more parameters includes one or more vessel locations.

14. The system of claim 11 , wherein the one or more parameters further includes one or more of, speed of flow, off resonance effects or vessel class proportions.

15. The system of claim 11 , wherein the data is obtained as a part of a planning process acquisition and the one or more parameters are derived from data from the planning process acquisition to provide an estimate of the vessel locations within the labelling region.

16. The system of claim 15 , wherein the one or more parameters represent a relationship between the true vessel locations in the region of interest and the vessel locations estimated from the planning process acquisition.

17. The system of claim 11 , wherein the estimation is derived from a marginalized a posteriori distribution of the VE-ASL data within the region of interest to derive point estimates of the location of the one or more vessels.

18. The system of claim 17 , wherein the estimation is derived from a maximum a posteriori (MAP) optimization.

19. The system of claim 11 , wherein the analysis is applicable to all types of vessel encoded arterial spin labelling (VE-ASL) data acquisitions.

20. The method of claim 1 , wherein, the estimating of step (B) includes inferring or estimating a rigid body transformation, thereby reducing the number of degrees-of-freedom of the analysis.

Assignees

• Isis Innovation
• Univ Oxford Innovation Ltd

Inventors

• Chappell Michael
• Woolrich Mark
• Okell Thomas

Classifications

• G01R33/56366

  Perfusion imaging · CPC title

• A61B5/0263 Primary

  using NMR · CPC title

• G01R33/56333

  Involving spatial modulation of the magnetization within an imaged region, e.g. spatial modulation of magnetization [SPAMM] tagging (perfusion imaging based on arterial spin tagging G01R33/56366) · CPC title