Imaging apparatus and method with compensation for object motion

• US 5,602,891 A
• Filed: 11/13/1995
• Issued: 02/11/1997
• Est. Priority Date: 11/13/1995
• Status: Expired due to Fees

• Current Assignee: Beth Israel Hospital
• Original Assignee: Beth Israel
• Inventors: Pearlman, Justin D.
• Primary Examiner(s): Porta, David P.
• Assistant Examiner(s): Bruce, David Vernon
• Application Number:
  US08/558,019
• Time in Patent Office: 456 Days
• Field of Search: 250/363.01, 250/363.02, 250/363.04, 250/369, 250/370.08, 250/370.09, 250/370.1, 250/370.11, 378/8, 378/62, 378/901, 364/413.14, 364/413.23
• US Class Current: 378/62
• CPC Class Codes: A61B 6/027 characterised by the use of a particular data acquisition trajectory, e.g. helical or spiral
• CPC Class Codes: A61B 6/463 characterised by displaying multiple images or images and diagnostic data on one display
• CPC Class Codes: A61B 6/466 adapted to display 3D data
• CPC Class Codes: A61B 6/481 involving the use of contrast agents
• CPC Class Codes: A61B 6/504 for diagnosis of blood vessels, e.g. by angiography
• CPC Class Codes: A61B 6/507 for determination of haemodynamic parameters, e.g. perfusion CT
• CPC Class Codes: A61B 6/5264 due to motion
• CPC Class Codes: G06T 15/10 Geometric effects
• CPC Class Codes: Y10S 378/901 Computer tomography program or processor

Abstract

An apparatus and method for generating a data set representation of an object or tissue are described. Two image representation data sets for the object or tissue are generated by a CT or MRI scan or other known imaging process. The second data set is preferably generated with enhanced contrast between background tissue and the target tissue of interest, e.g., blood vessels, such as by the injection of a vascular contrast enhancing agent. The first data set is viewed as the union of plural data value neighborhoods, with each neighborhood preferably surrounding a central data value. Each data value of the second data set is compared to a neighborhood in the first data set. If a data value in the second data set is incompatible with the neighborhood, then it is concluded that the data value in the second data set is representative of contrast-enhanced target tissue and is therefore used to generate the final image data set of the target tissue.

Claims

1. A method of generating a representation of target tissue in a region of tissue comprising:

generating a first representation of the region of tissue;

generating a second representation of the region of tissue;

forming an association between each of a plurality of data values of the second representation and a corresponding neighborhood of data values of the first representation;

comparing data values of the second representation with data values of respective associated neighborhoods of the first representation to distinguish target tissue data values of the second representation from background tissue data values of the second representation; and

generating the representation of the target tissue using the target tissue data values of the second representation.

2. The method of claim 1 further comprising enhancing contrast in the second representation between the target tissue and the background tissue in the region of interest.

3. The method of claim 1 wherein each of the first and second representations is generated by performing a scan of the region of tissue with a CT imaging system.

4. The method of claim 1 wherein each of the first and second representations is generated by performing a scan of the region of tissue with an MRI imaging system.

5. The method of claim 1 wherein a data value of the first representation comprises summary data for a neighborhood of data values in the first representation.

6. The method of claim 5 wherein the summary data for a neighborhood of data values includes maximum pixel intensity information for all of the pixels represented by data values in the neighborhood.

7. The method of claim 5 wherein the summary data for a neighborhood of data values includes minimum pixel intensity information for all of the pixels represented by data values in the neighborhood.

8. The method of claim 5 wherein the summary data for a neighborhood of data values includes information regarding intensity statistical moments for pixels represented by the data values in the neighborhood.

9. The method of claim 1 wherein a partial volume data value of the second representation that represents both background tissue and target tissue is used to generate the representation of the target tissue.

10. The method of claim 1 wherein the step of comparing comprises generating an error value defined by a difference between a data value of the second representation and a data value of the first representation.

11. The method of claim 10 wherein the step of generating the representation of the target tissue comprises computing at least one curve fit to error values associated with data values of the second representation.

12. The method of claim 1 wherein each neighborhood of data values of the second representation represents a volume of tissue within the region of tissue.

13. The method of claim 12 wherein the volume is a rectangular solid.

14. The method of claim 1 further comprising computing a displacement between a data value of the first representation and a corresponding data value of the second representation.

15. The method of claim 1 further comprising classifying data values of the second representation as background tissue values, target tissue values or uncertain.

16. The method of claim 15 further comprising:

analyzing data values classified as uncertain; and

reclassifying uncertain data values as background tissue values or target tissue values.

17. The method of claim 1 further comprising generating an image of the target tissue from the representation of the target tissue.

18. The method of claim 1 wherein data values of the second representation are compared with data values of neighborhoods of the first representation using a rolling buffer process.

19. A method of classifying data values in a data set representative of tissue in a region of tissue comprising:

providing a first data set;

generating a second data set representative of the region of tissue;

forming an association between each of a plurality of data values of the second data set and a corresponding neighborhood of data values of the first data set;

comparing data values of the second data set with data values of respective associated neighborhoods of the first data set; and

classifying data values of the second data set according to the comparison performed during the comparing step between data values of the second data set and data values of the first data set.

20. An imaging system for generating a representation of target tissue in a region of tissue comprising:

a data collection subsystem comprising a source of radiation and a detector, said detector generating a first representation of the region of tissue and a second representation of the region of tissue;

a data processor that forms an association between each of a plurality of data values of the second representation and a corresponding neighborhood of data values of the first representation, that compares data values of the second representation with data values of respective associated neighborhoods of the first representation to distinguish target tissue data values of the second representation from background tissue data values of the second representation and that generates the representation of the target tissue using the target tissue data values of the second representation.

21. The imaging system of claim 20 wherein the second representation has enhanced contrast between the target tissue and the background tissue in the region of tissue.

22. The imaging system of claim 20 wherein the data collection subsystem comprises a CT scanning system.

23. The imaging system of claim 20 wherein the data collection subsystem comprises an MRI scanning system.

24. The imaging system of claim 20 wherein a data value of the first representation comprises summary data for a neighborhood of data values of the first representation.

25. The imaging system of claim 24 wherein the summary data for a neighborhood of data values includes maximum pixel intensity information for all of the pixels represented by the data values in the neighborhood.

26. The imaging system of claim 24 wherein the summary data for a neighborhood of data values includes minimum pixel intensity information for all of the pixels represented by data values in the neighborhood.

27. The imaging system of claim 24 wherein the summary data for a neighborhood of data values includes information regarding intensity statistical moments for pixels represented by the data values in the neighborhood.

28. The imaging system of claim 20 wherein a partial volume data value of the second representation that represents both background tissue and target tissue is used by the data processor to generate the representation of the target tissue.

29. The imaging system of claim 20 wherein the data processor generates an error value defined by a difference between a data value of the second representation and a data value of the first representation.

30. The imaging system of claim 29 wherein the data processor computes at least one curve fit to error values associated with data values of the second representation.

31. The imaging system of claim 20 wherein each neighborhood of data values of the second representation represents a volume of tissue within the region of tissue.

32. The imaging system of claim 31 wherein the volume is a rectangular solid.

33. The imaging system of claim 20 wherein the data processor computes a displacement between a data value of the first representation and a corresponding data value of the second representation.

34. The imaging system of claim 20 wherein the data processor classifies data values of the second representation as background tissue values, target tissue values or uncertain.

35. The imaging system of claim 34 wherein the data processor analyzes data values classified as uncertain and reclassifies uncertain data values as background tissue values or target tissue values.

36. The imaging system of claim 20 further comprising a display for presenting an image of the target tissue from the representation of the target tissue.

37. A method of generating an image of blood vessels in a region of tissue comprising:

scanning the region of tissue to generate a first data set for the region of tissue;

introducing a vascular contrast enhancing agent into the blood vessels in the region of tissue;

after introducing the vascular contrast enhancing agent, scanning the region of tissue to generate a second data set for the region of tissue;

forming an association between each of a plurality of data values of the second data set and a corresponding neighborhood of data values of the first data set;

comparing data values of the second data set with data values of respective associated neighborhoods of the first data set to distinguish contrast-enhanced blood vessel data values of the second data set from background tissue data values of the second data set; and

generating a data set for the blood vessels using the contrast-enhanced blood vessel data values of the second data set.

38. The method of claim 37 wherein each of the first and second data sets is obtained by scanning the region of tissue with a CT imaging system.

39. The method of claim 37 wherein the step of comparing comprises generating an error value defined by a difference between a data value of the second data set and data value of the first data set.

40. The method of claim 37 wherein each neighborhood of data values of the second data set represents a volume of tissue within the region of tissue.

41. The method of claim 40 wherein the volume is a rectangular solid.

42. The method of claim 37 further comprising computing a displacement between a data value of the first data set and a corresponding data value of the second data set.

43. The method of claim 37 further comprising classifying data values of the second data set as background tissue values, blood vessel tissue values or uncertain.

44. The method of claim 37 further comprising generating an image of the blood vessels from the data set for the blood vessels.