Methods and apparatus for estimating a material composition of an imaged object

• US 20030072409A1
• Filed: 10/12/2001
• Published: 04/17/2003
• Est. Priority Date: 10/12/2001
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Eberhard, Jeffrey Wayne, Claus, Bernhard Erich Hermann, Tkaczyk, John Eric, Kaufhold, John Patrick, Gonzalez Trotter, Dinko E.
• Granted Patent: US 6,674,835 B2
• Time in Patent Office: Days
• US Class Current: 378/53
• CPC Class Codes: A61B 5/4869 Determining body composition
• CPC Class Codes: A61B 6/502 for diagnosis of breast, i.e. mammography
• CPC Class Codes: A61B 6/583 using calibration phantoms
• CPC Class Codes: G01N 23/044 using laminography or tomosynthesis

Abstract

A method for estimating a material composition of an imaged object using an imaging system. The imaging system includes a radiation source and a digital detector. The method also includes scanning a plurality of calibration phantoms with varying material composition to acquire a plurality of reference calibration images, estimating an attenuation coefficient thickness product for each pixel in the reference calibration images, and estimating a material composition of a region of interest using the estimated pixelwise coefficient thickness product.

Claims

1. A method for estimating a material composition of an imaged object using an imaging system, the imaging system including a radiation source and a digital detector, said method comprising:

scanning a plurality of calibration phantoms with varying material composition to acquire a plurality of reference calibration images;

estimating an attenuation coefficient thickness product for each pixel in the reference calibration images; and

estimating a material composition of a region of interest using the estimated pixelwise coefficient thickness product.

2. A method in accordance with claim 1 wherein the varying material compositions include a plurality of breast-equivalent materials.

3. A method in accordance with claim 1 wherein estimating a material composition of an imaged object further comprises adjusting the reference calibration images to to generate a plurality of scatter-corrected and gain-corrected images.

4. A method in accordance with claim 3 wherein adjusting the reference calibration images comprises normalizing out a plurality of imaging error conditions.

5. A method in accordance with claim 4 wherein normalizing out a plurality of imaging conditions include correcting the reference calibration images to remove the effects of scatter, electronic drift in the detector, geometric effects, and non-uniform detector counting characteristics.

6. A method in accordance with claim 1 wherein estimating an attenuation coefficient thickness product for each pixel in the reference calibration images includes transforming a plurality of photon counts to a measure that is linear in the attenuation coefficient thickness product.

7. A method in accordance with claim 6 wherein transforming a plurality of photon counts to a measure that is linear in the attenuation coefficient thickness product includes transforming in accordance with y_phantom(i, j)=−

log(I_phantom(i, j)).

8. A method in accordance with claim 1 wherein to estimate a composition of a region of interest of an object further comprises estimating a composition of a region of interest using a model-based estimation including a theoretical model.

9. A method in accordance with claim 1 wherein the region of interest of an object is a region of interest of a breast.

10. A method for estimating a material composition of an imaged object using an imaging system, the imaging system including a radiation source and a digital detector, said method comprising:

scanning a plurality of calibration phantoms comprising a plurality of breast-equivalent materials to acquire a plurality of reference calibration images;

estimating an attenuation coefficient thickness product for each pixel in the reference calibration images comprises transforming a plurality of photon counts to a measure that is linear in the attenuation coefficient in accordance with y_phantom(i, j)=−

log(I_phantom(i, j)) and wherein estimating an attenuation coefficient further comprises adjusting the reference calibration images to remove the effects of scatter, electronic drift in the detector, geometric effects, and non-uniform detector counting characteristics generate a plurality of scatter-corrected and gain corrected images; and

estimating a material composition of a region of interest using the estimated coefficient thickness product.

11. A medical imaging system for estimating a material composition of an imaged object, said medical imaging system comprising:

a detector array;

at least one radiation source; and

a computer coupled to said detector array and radiation source and configured to;

scan a plurality of calibration phantoms comprising varying material composition to acquire a plurality of reference calibration images;

estimate an attenuation coefficient thickness product for each pixel in the reference calibration images; and

estimate a material composition of a region of interest using the estimated coefficient thickness product.

12. A medical imaging system in accordance with claim 11 wherein to scan a plurality of calibration phantoms comprising varying material compositions, said computer further configured to scan a plurality of calibration phantoms comprising a plurality of breast-equivalent materials.

13. A medical imaging system in accordance with claim 11 wherein to estimate a material composition of an imaged object, said computer further configured to adjust the reference calibration images to generate a plurality of scatter-corrected and gain corrected images.

14. A medical imaging system in accordance with claim 13 wherein to adjust the reference calibration images, said computer further configured to normalize out a plurality of imaging error conditions.

15. A medical imaging system in accordance with claim 14 wherein to normalize out a plurality of imaging conditions, said computer further configured to remove the effects of scatter, electronic drift in the detector, geometric effects, and non-uniform detector counting characteristics.

16. A medical imaging system in accordance with claim 11 wherein to estimate an attenuation coefficient thickness product for each pixel in the reference calibration images, said computer further configured to transform a plurality of photon counts to a measure that is linear in the attenuation coefficient thickness product.

17. A medical imaging system in accordance with claim 16 wherein to transform a plurality of photon counts to a measure that is linear in the attenuation coefficient thickness product, said computer further configured to transform in accordance with y_phamtom(i, j)=−

log(I_phamtom(i, j)).

18. A medical imaging system in accordance with claim 11 wherein to estimate a composition of a region of interest of an object, said computer further configured to estimate a composition of a region of interest using a model-based estimation including a theoretical model.

19. A medical imaging system in accordance with claim 11 wherein to estimate a composition of a region of interest of an object, said computer further configured to estimate a composition of a region of interest of a breast.

20. A medical imaging system for estimating a material composition of an imaged object, said medical imaging system comprising:

a detector array;

at least one radiation source; and

a computer coupled to said detector array and radiation source and configured to;

scan a plurality of calibration phantoms comprising a plurality of breast-equivalent materials to acquire a plurality of reference calibration images;

estimate an attenuation coefficient thickness product for each pixel in the reference calibration images, wherein to estimate an attenuation coefficient thickness product further comprises transforming a plurality of photon counts to a measure that is linear in the attenuation coefficient in accordance with y_phantom (i, j)=−

log(I_phantom(i, j)) and adjusting the reference calibration images to remove the effects of scatter, electronic drift in the detector, geometric effects, and non-uniform detector counting characteristics generate a plurality of scatter-corrected and gain corrected images; and

estimate a material composition of a region of interest using the estimated coefficient thickness product.

21. A computer readable medium encoded with a program executable by a computer for reconstructing a three-dimensional dataset representative of an imaged object, said program configured to instruct the computer to:

scan a plurality of calibration phantoms comprising varying material composition to acquire a plurality of reference calibration images;

estimate an attenuation coefficient thickness product for each pixel in the reference calibration images; and

estimate a material composition of a region of interest using the estimated coefficient thickness product.

22. A computer readable medium in with claim 21 wherein to scan a plurality of calibration phantoms comprising varying material compositions, said program further configured to scan a plurality of calibration phantoms comprising a plurality of breast-equivalent materials.

23. A computer readable medium in accordance with claim 21 wherein to estimate a material composition of an imaged object, said program further configured to adjust the reference calibration images to generate a plurality of scatter-corrected and gain corrected images.

24. A computer readable medium in accordance with claim 23 wherein to adjust the reference calibration images, said program further configured to normalize out a plurality of imaging error conditions.

25. A computer readable medium in accordance with claim 24 wherein to normalize out a plurality of imaging conditions, said program further configured to remove the effects of scatter, electronic drift in the detector, geometric effects, and non-uniform detector counting characteristics.

26. A computer readable medium in accordance with claim 21 wherein to estimate an attenuation coefficient thickness product for each pixel in the reference calibration images, said program further configured to transform a plurality of photon counts to a measure that is linear in the attenuation coefficient thickness product.

27. A computer readable medium in accordance with claim 26 wherein to transform a plurality of photon counts to a measure that is linear in the attenuation coefficient thickness product, said program further configured to transform in accordance with y_phantom (i, j)=−

log(I_phantom(i, j)).

28. A computer readable medium in accordance with claim 21 wherein to estimate a composition of a region of interest of an object, said program further configured to estimate a composition of a region of interest using a model-based estimation including a theoretical model.

29. A computer readable medium in accordance with claim 21 wherein to estimate a composition of a region of interest of an object, said program further configured to estimate a composition of a region of interest of a breast.

30. A computer readable medium encoded with a program executable by a computer for reconstructing a three-dimensional dataset representative of an imaged object, said program configured to instruct the computer to:

scan a plurality of calibration phantoms comprising a plurality of breast-equivalent materials to acquire a plurality of reference calibration images;

estimate an attenuation coefficient thickness product for each pixel in the reference calibration images, wherein to estimate an attenuation coefficient thickness product further comprises transforming a plurality of photon counts to a measure that is linear in the attenuation coefficient in accordance with y_phamtom(i, j)=−

log(I_phamtom(i, j)) and adjusting the reference calibration images to remove the effects of scatter, electronic drift in the detector, geometric effects, and non-uniform detector counting characteristics generate a plurality of scatter-corrected and gain corrected images; and

estimate a material composition of a region of interest using the estimated coefficient thickness product.