Method and apparatus for quantifying tissue fat content

1. A method for obtaining data, said method comprising performing at least one scout scan, acquiring x-ray multi-energy computed tomography (MECT) image data, automatically segmenting the MECT image data using a priori anatomical information derived from at least one scout scan to determine a region of interest, decomposing the MECT image data into a first density map representative of fatty tissue and a second density map representative of lean tissue to quantify tissue fat content in the region of interest, and using said quantified tissue fat content to detect a clinical condition in the region of interest.

2. A method in accordance with claim 1 wherein decomposing the MECT image data comprises decomposing the MECT image data using at least one of a CT number difference decomposition, a Compton and photoelectric decomposition, a basis material decomposition (BMD), and a logarithm subtraction decomposition (LSD).

3. A method in accordance with claim 1 further comprising merging the first density map with the second density map to obtain a density image that quantifies fat content.

4. A method in accordance with claim 3 wherein merging the first density map with the second density map comprises generating a fat/lean ratio map on a pixel by pixel basis between the merged first and second density maps.

5. A method in accordance with claim 4 further comprising superimposing the ratio map on an anatomical image of the tissue.

6. A method in accordance with claim 5 wherein superimposing the ratio map further comprises displaying the ratio map in color as an overlay to a grayscale anatomical image of the tissue.

7. A method in accordance with claim 3 further comprising segmenting the merged first and second density maps to determine a region of interest.

8. A method in accordance with claim 7 wherein segmenting the merged first and second density maps comprises segmenting the merged first and second density maps using at least one of pixel-value thresholding, iterative thresholding, k-means segmentation, edge detection, edge linking, curve fitting, curve smoothing, 2D/3D morphological filtering, region growing, fuzzy clustering, image/volume measurements, heuristics, knowledge-based rules, decision trees, and neural networks.

9. A method in accordance with claim 7 wherein segmenting the merged first and second density maps comprises manually segmenting the merged first and second density maps using displayed image data.

10. A method in accordance with claim 7 wherein segmenting the merged first and second density maps comprises automatically segmenting the merged first and second density maps using an algorithm.

11. A multi-energy computed tomography (MECT) system comprising:

at least one x-ray radiation source;

at least one x-ray radiation detector; and

a computer operationally coupled to said radiation source and said radiation detector, said computer configured to:

receive data regarding at least one scout scan of a patient;

receive data regarding a first energy spectrum of an x-ray computed tomography scan of tissue of the patient;

receive data regarding a second energy spectrum of an x-ray computed tomography scan of the tissue;

decompose and segment said received data to identify regional fatty tissue and lean tissue, wherein said segmenting comprises automatic segmentation using a priori anatomical information derived from the at least one scout scan to identify a region of interest in the tissue;

use said identification of regional fatty and lean tissue to detect a clinical condition in the tissue.

12. A MECT system in accordance with claim 11 wherein the tissue is liver tissue, and said computer configured to use said identification of regional fatty and lean tissue to detect a clinical condition in the tissue further comprises said computer configured to use said identification of regional fatty and lean tissue to detect a fatty liver condition.

13. A MECT system in accordance with claim 12 wherein said computer configured to decompose said received data using at least one of a CT number difference decomposition, a Compton and photoelectric decomposition, a basis material decomposition (BMD), and a logarithm subtraction decomposition (LSD).

14. A MECT system in accordance with claim 12 wherein said computer further configured to:

decompose said received data to generate a first density map representative of fatty tissue and a second density map representative of lean tissue; and

merge said first density map with said second density map to generate a fat/lean ratio map on a pixel by pixel basis between said first and second density maps.

15. A MECT system in accordance with claim 14 wherein said computer further configured to superimpose said fat/lean ratio map on an anatomical image of the tissue.

16. A MECT system in accordance with claim 13 wherein said computer further configured to:

said computer further configured to:

create a first density map representative of fatty tissue and a second density map representative of lean tissue;

merge said first density map with said second density map; and

said using said identification of regional fatty and lean tissue further comprises using fatty tissue and lean tissue characterizations to detect a fatty liver condition.

17. A MECT system in accordance with claim 11 wherein said computer configured to segment said merged first and second density map using at least one of pixel-value thresholding, an iterative thresholding, a k-means segmentation, an edge detection, an edge linking, a curve fitting, a curve smoothing, a 2D/3D morphological filtering, a region growing, a fuzzy clustering, image/volume measurements, a heuristic, knowledge-based rules, decision trees, and neural networks.

18. A computer readable medium embedded with a program configured to instruct a computer to:

receive data regarding at least one scout scan of a patient;

receive data regarding a first energy spectrum of an x-ray multi-energy computed tomographic (MECT) scan of tissue including a liver;

receive data regarding a second energy spectrum of an x-ray scan of the tissue;

decompose said received data to generate a first density map representative of fatty tissue and a second density map representative of lean tissue;

merge said first density map with said second density map to generate a fat/lean ratio map;

automatically segment said merged first and second density map using a priori anatomical information derived from the at least one scout scan to determine a region of interest; and

use fatty tissue and lean tissue characterizations to detect a fatty liver condition.

19. A computer readable medium in accordance with claim 18 wherein said computer readable medium configured to instruct the computer to decompose said received data using at least one of a CT number difference decomposition, a Compton and photoelectric decomposition, a basis material decomposition (BMD), and a logarithm subtraction decomposition (LSD).

20. A computer configured to:

receive data regarding at least one scout scan of a patient;

receive an x-ray MECT image data for tissue;

decompose and segment said image data into a first density map representative of fatty tissue within a region of interest and a second density map representative of lean tissue within a region of interest, wherein said segmenting comprises automatic segmenting using a priori anatomical information derived from said at least one scout scan of the patient; and

decompose and segment said image data to thereby identify a contrast agent consisting of ingested dietary fat.

21. A method for obtaining data, said method comprising performing at least one scout scan to acquire scout image data, obtaining x-ray MECT image data, and, based upon information derived from the scout scan, segmenting and decomposing the x-ray MECT image data into a first density map representative of fatty tissue and a second density map representative of lean tissue to quantify tissue fat content in the region of interest, and using said quantified tissue fat content to detect a clinical condition in the region of interest.