METHOD AND APPARATUS FOR THE ASSESSMENT OF MEDICAL IMAGES

• US 20140307936A1
• Filed: 12/14/2012
• Published: 10/16/2014
• Est. Priority Date: 12/15/2011
• Status: Active Grant

• Current Assignee: Commonwealth Scientific & Industrial Research Organisation (Commonwealth Of Australia As Represented By Department Of Industry, Innovation, Climate Change, Science, Research And Tertiary Education)
• Original Assignee: Commonwealth Scientific & Industrial Research Organisation (Commonwealth Of Australia As Represented By Department Of Industry, Innovation, Climate Change, Science, Research And Tertiary Education)
• Inventors: Dore, Vincent, Salvado, Olivier, Dowson, Nicholas Delanie Hirst, Mejan-Fripp, Jurgen, Rowe, Christopher, Villemagne, Victor, Zhou, Luping
• Granted Patent: US 9,361,686 B2
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/7425 Displaying combinations of multiple images regardless of image source, e.g. displaying a reference anatomical image with a live image geometric transforms for image registration G06T3/14; enhancement using image fusion G06T5/50; algorithms for registration of images G06T7/30
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 8/5207 involving processing of raw data to produce diagnostic data, e.g. for generating an image reconstruction G06T11/00
• CPC Class Codes: F04C 2270/041 Controlled or regulated
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10104 Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/20216 Image averaging
• CPC Class Codes: G06T 2207/20221 Image fusion; Image merging
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 7/0014 using an image reference approach
• CPC Class Codes: G06T 7/0016 involving temporal comparison
• CPC Class Codes: G06T 7/33 using feature-based methods

Abstract

A method of determining the degree of uptake of a PET maker in an individual candidate PET scan, the method including the steps of: (a) calculating a series of representative matched controlled PET and MRI templates for a series of controlled sample scans of individuals; (b) computing a series of brain surfaces from the matched templates; (c) aligning the individual candidate PET scan with the candidate templates; (d) aligning the candidate PET images with the series of brain surfaces; (e) selecting a predetermined (M) best candidate templates for each surface location based on a similarity measure between the candidate PET values and the corresponding controlled PET scans; (f) computing M weights for each surface location, utilizing a corresponding MRI tissue map; (g) utilizing the M weights to combine a corresponding M template tissue indicators from corresponding MRI templates into an average brain surface indicator.

Claims

1. A method of determining the degree of uptake of a PET maker in an individual candidate PET scan, the method of including the steps of:

(a) calculating a series of representative matched controlled PET and MRI templates for a series of controlled sample scans of individuals;

(b) computing a series of brain surfaces from the matched templates;

(c) aligning the individual candidate PET scan with the candidate templates;

(d) aligning the series of brain surfaces with the candidate PET images;

(e) selecting a predetermined (M) best candidate templates for each surface location based on a similarity measure between the candidate PET values and the corresponding controlled PET scans;

(f) computing M weights for each surface location, utilizing a corresponding MRI tissue map;

(g) utilizing the M weights to combine a corresponding M template tissue indicators from corresponding MRI templates into an average brain surface indicator.

2. A method as claimed in claim 1 further comprising the step of:

(h) combining the templates brain surfaces to create an averaged brain surface for display.

3. A method as claimed in claim 1 wherein step (c) further comprises utilizing candidate CT or X-Ray scan data in the alignment of the candidate PET images with the series of candidate templates.

8. A method as claimed in claim 1 wherein the templates and candidate images are segmented.

15. A method as claimed in claim 1 wherein said step (g) includes utilising the M weight as a weighted average in combining the corresponding template tissue indicators.

16. A method as claimed in claim 1 wherein step (g) includes utilising the M weights in a voting algorithm to determine a corresponding template tissue indicator.

17. An apparatus when implementing the method of claim 1.

4. A method of determining the degree of uptake of an imaging marker in an individual candidate imaging marker scan, the method including the steps of:

(a) calculating a series of representative matched controlled imaging marker scans and tissue marker templates for a series of controlled sample scans of individuals;

(b) computing a series of internal body delineation surfaces from the matched templates;

(c) aligning the individual candidate imaging marker scan with the candidate templates;

(d) aligning the individual candidate imaging marker scan with the series of body delineation surfaces;

(e) selecting a predetermined (M) best candidate templates for each surface location based on a similarity measure between the candidate imaging marker values and the corresponding controlled imaging marker scans;

(f) computing M weights for each surface location, utilizing a corresponding tissue marker map;

(g) utilizing the M weights to combine a corresponding M template tissue indicators from corresponding tissue templates into an average brain surface indicator.

5. A method as claimed in claim 4 wherein the imaging marker scans comprise Positron Emission Tomography (PET) scans or a Single-photon emission computed tomography (SPECT) scans.

6. A method as claimed in claim 4 wherein each tissue marker template is calculated from images of different subjects with known image properties.

7. A method as claimed in claim 6 wherein the multiple tissue templates are selected from a wider set of templates in accordance with similar characteristics of the candidate and subjects with known image properties.

9. A method of determining the brain uptake if an imaging marker in a subject image, the method including the steps of:

preparing a series of brain templates from controlled sample images, the templates including coregistering a series of controlled sample images, and creating a probability map containing an approximation of the likelihood that a particular atlas voxel contains grey matter;

determining from the brain templates, a correspondence surface between grey and white matter interfaces;

for the candidate subject image, mapping the candidate image to a corresponding brain template; and

mapping the correspondence in grey matter uptake for the subject image for grey area portions of the brain templates.

10. A method as claimed in claim 9 wherein said controlled sample images include both PET and MR images.

11. A method as claimed in claim 9 wherein said subject image is a PET image.

12. A method as claimed in claim 9 wherein the correspondence is measured in a predetermined direction or volume relative to the surface location between grey and white matter or gray matter and CSF.

13. A method as claims in claim 9 wherein said mapping occurs for multiple templates.

14. A method as claimed in claim 13, wherein said multiple mappings are combined utilising a Bayesian network.