Analysis of successive data sets

• US 20020168095A1
• Filed: 02/08/2002
• Published: 11/14/2002
• Est. Priority Date: 02/13/2001
• Status: Active Grant

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Breeuwer, Marcel, Spreeuwers, Lieuwe Jan, Haselhoff, Eltjo Hans
• Granted Patent: US 7,376,253 B2
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: A61B 6/507 for determination of haemodynamic parameters, e.g. perfusion CT
• CPC Class Codes: G06T 2207/10108 Single photon emission computed tomography [SPECT]
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/11 Region-based segmentation

Abstract

The invention relates to the analysis of successive data sets. A local intensity variation is formed from such successive data sets, that is, from data values in successive data sets at corresponding positions in each of the data sets. A region of interest is localized in the individual data sets on the basis of the local intensity variation. In particular the time derivative of the local intensity variation is used to localize the region of interest. The invention can be used notably for cardiological applications so as to separate the image of the myocardium from a sequence of 3D magnetic resonance reconstruction images.

Claims

1. A method of analyzing successive data sets, in which method

the individual data sets comprise data elements which assign data values to spatial positions,

a local intensity variation [I(x,t)] is established from data values in successive data sets in corresponding spatial positions, and

on the basis of the local intensity variation a region of interest is localized from one or more of the successive data sets,

the local intensity variation in the region of interest is in conformity with a predetermined property.

2. A method of analyzing successive data sets as claimed in claim 1, in which the local intensity variation is established for respective blocks of several data elements.

3. A method of analyzing successive data sets as claimed in claim 1 or 2, in which method

the region of interest is localized on the basis of variations in the local intensity variation, notably on the basis of a time derivative

$\frac{\partial }{}$

I

(x,t)∂

t

of the local intensity variation.

4. A method of analyzing successive data sets as claimed in claim 3, in which method

the region of interest is localized by localizing blocks of data elements in which

the variations in the local intensity variation are larger than a predetermined ceiling value and/or by localizing blocks of data elements in which the variations in the local intensity variation are smaller than a predetermined bottom value.

5. A method of analyzing successive data sets as claimed in claim 1, in which method

data elements are classified in one or more individual data sets,

the classification of the data elements in the relevant data set (sets) indicating whether the relevant data element belongs to the region of interest or not.

6. A method of analyzing successive data sets as claimed in claim 5, in which method

the classification is performed on the basis of a measure of similarity of the local intensity variation and a reference intensity variation in the region of interest.

7. A method of analyzing successive data sets as claimed in claim 5, in which method

the classification is performed on the basis of a correlation of the local intensity variation with a mean intensity variation in the region of interest.

8. A method of analyzing successive data sets as claimed in claim 1, in which method

parts with spatial gradients of data values are masked in individual data sets in as far as the modulus of the spatial gradients in the relevant parts exceeds a predetermined acceptable gradient modulus.

9. A method of analyzing successive data sets as claimed in claim 1, in which method the successive data sets are made to correspond to one another.

13. A method as claimed in claim 3, in which method

a mask is derived from the time derivative of the local intensity variation for an individual data set, and

the region of interest is segmented from the relevant data set by means of the mask.

15. A method as claimed in claim 3, in which method a spatial distribution of the time derivative of the local intensity variation is reproduced for an individual data set.

10. A method of analyzing successive data sets, in which method

a plurality of regions of interest is localized on the basis of the local intensity variation the local intensity variation in said regions of interest being in conformity with a predetermined property, and

maximum intensity projections (MIPs) are determined for the respective regions of interest, and

a feature image is formed from differences between said maximum intensity projections.

11. A method of analyzing successive data sets as claimed in claim 10, in which method a center of the region of interest is determined in the feature image.

12. A method of analyzing successive data sets as claimed in claim 11, in which method

the feature image is transformed to pole co-ordinates with said center as the origin, and

a boundary of the region of interest is localized in said transformed feature image.

14. A method as claimed in claim 13, in which method the mask is derived by applying a threshold filter to the time derivative of the local intensity variation for the relevant data set.

16. An analysis system for analyzing successive data sets,

in which the individual data sets comprise data elements which assign data values to spatial positions,

which analysis system is arranged to

establish a local intensity variation [I(x,t)] from data values in successive data sets in corresponding spatial positions, and

on the basis of the local intensity variation a region of interest is localized from one or more of the successive data sets,

the local intensity variation in the region of interest is in conformity with a predetermined property.

17. A computer program for analyzing successive data sets,

in which the individual data sets comprise data elements which assign data values to spatial positions,

which computer system comprises instructions for

establishing a local intensity variation [I(x,t)] from data values in successive data sets in corresponding spatial positions, and

localizing, on the basis of the local intensity variation, a region of interest from one or more of the successive data sets,

the local intensity variation in the region of interest being in conformity with a predetermined property.