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1. (WO2010082101) REGIONAL RECONSTRUCTION AND QUANTITATIVE ASSESSMENT IN LIST MODE PET IMAGING
Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

CLAIMS

Having thus described the preferred embodiments, the invention is now claimed to be:

1. A method for reconstructing list mode data, the method comprising: reconstructing all list mode data of a list mode data set (30, 160) to generate a first reconstructed image (32, 62); selecting a sub-set of the list mode data set; and reconstructing the sub-set of the list mode data set to generate an enhanced reconstructed image (84, 86).

2. The method as set forth in claim 1, wherein the selecting comprises: identifying a region of interest in image space; and selecting a sub-set of the list mode data set comprising list mode data contributing to image content of the region of interest in image space.

3. The method as set forth in claim 2, wherein the identifying a region of interest in image space comprises: identifying a region of interest in image space based on a feature delineated in the first reconstructed image.

4. The method as set forth in any one of claims 2-3, wherein the reconstructing the sub-set comprises: adjusting at least some list mode data of the sub-set to compensate for local motion; and reconstructing the adjusted sub-set to generate an enhanced reconstructed image (84, 86) including local motion compensation.

5. The method as set forth in any one of claims 1-4, wherein the reconstructing the sub-set comprises: reconstructing using at least one of a partial volume correction and a system point spread function.

6. The method as set forth in any one of claims 1-5, wherein the list mode data set (30, 160) comprises positron emission tomography (PET) list mode data, and the reconstructing to generate a first reconstructed image (32, 62) comprises: reconstructing all list mode data of the list mode data set to generate a first reconstructed image as a standard image comprising 4 mm3 voxels.

7. The method as set forth in any one of claims 1-6, wherein the reconstructing to generate a first reconstructed image (32, 62) is performed at an imaging facility and the selecting a sub-set is performed at a treatment planning facility.

8. The method as set forth in claim 7, wherein the reconstructing to generate an enhanced reconstructed image (84, 86) is performed at the treatment planning facility, and the method further comprises: transferring at least the sub-set of the list mode data set (30, 160) from the imaging facility to the treatment planning facility.

9. The method as set forth in claim 7, wherein the reconstructing to generate an enhanced reconstructed image (84, 86) is performed at the imaging facility, and the method further comprises: generating identifying information (72) sufficient for identifying the sub-set at the treatment planning facility; transferring the identifying information from the treatment planning facility to the imaging facility; and selecting the sub-set of the list mode data set at the imaging facility based on the identifying information.

10. The method as set forth in claim 9, wherein the identifying information (72) includes at least one of (i) an identification of a region of interest in image space and (ii) an identification of an image resolution in image space.

11. The method as set forth in any one of claims 1-10, wherein the reconstructing to generate an enhanced reconstructed image (84, 86) utilizes at least one reconstruction parameter different from the reconstructing to generate a first reconstructed image (32, 62).

12. The method as set forth in any one of claims 1-11, wherein the reconstructing to generate an enhanced reconstructed image (84, 86) utilizes a higher resolution reconstruction parameter than the reconstructing to generate a first reconstructed image (32, 62).

13. The method as set forth in any one of claims 1-12, wherein: the reconstructing to generate a first reconstructed image (32, 62) employs an iterative reconstruction algorithm; and the reconstructing to generate an enhanced reconstructed image (84, 86) employs a non-iterative reconstruction algorithm.

14. The method as set forth in any one of claims 1-13, further comprising: performing a quantitative diagnostic analysis on the enhanced reconstructed image

(84, 86).

15. The method as set forth in claim 14, wherein the quantitative diagnostic analysis comprises a standardized uptake value (SUV) analysis.

16. An image generation system comprising: a reconstruction module (24) configured to perform a standard reconstruction of a list mode data set to generate a standard reconstructed image (32, 62); and a re -reconstruction module (24, 70, 80, 82, 150, 152, 154) configured to perform a reconstruction other than the standard reconstruction of at least a portion of the list mode data set to generate an enhanced reconstructed image (84, 86).

17. The image generation system as set forth in claim 16, wherein: the reconstruction module (24) employs a set of standard reconstruction parameters including at least a standard image resolution, and the re -reconstruction module (24, 70, 80, 82, 150, 152, 154) includes an interactive re-reconstruction configuration engine (70) enabling user selection of reconstruction parameters used in the re-reconstruction.

18. The image generation system as set forth in claim 17, wherein the interactive re -reconstruction configuration engine (70) enables user selection of at least a region of interest in image space and the re -reconstruction module (24, 70, 80, 82, 150, 152, 154) further comprises: a region subset selection engine (82, 154) that selects a subset of the list mode data set (30, 160) that contributes to image content of the region of interest in image space as the portion of the list mode data set that is reconstructed to generate the enhanced reconstructed image (84, 86).

19. The image generation system as set forth in any one of claims 16-18, further comprising: a treatment planning module (42, 44, 142, 144) configured to generate a treatment plan (56), the treatment planning module including at least a medical image viewing station (46) and at least a portion (70, 150, 152, 154) of the re-reconstruction module (24, 70, 80, 82, 150, 152, 154).

20. The image generation system as set forth in claim 19, wherein the treatment planning module (42, 44, 142, 144) further comprises: an intensity-modulated radiation therapy planning engine (54) configured to generate a radiation therapy session plan (56).

21. The image generation system as set forth in any one of claims 19-20, wherein the treatment planning module (42, 44, 142, 144) does not include any portion of the reconstruction module (24).

22. The image generation system as set forth in any one of claims 19-21, wherein the treatment planning module (142, 144) includes the entire re -reconstruction module (70, 150, 152, 154) and does not include any portion of the reconstruction module

(24).

23. The image generation system as set forth in any one of claims 16-21, whe truction module (24) and the re-reconstruction module (24, 70, 80, 82) employ a common reconstruction engine (24).

24. An image generation method comprising: reconstructing an image (32, 62) from image data (30, 160); transferring the image to a treatment planning facility; at the treatment planning facility, selecting one or more parameters for a re-reconstruction that are different from parameters used in the initial reconstruction; and re-reconstructing an updated image (84, 86) from at least a portion of the image data using the selected one or more parameters for the re-reconstruction.

25. The image generation method as set forth in claim 24, wherein the re-reconstructing is performed at the treatment planning facility.

26. The image generation method as set forth in claim 24, wherein the re-reconstructing is performed at a facility other than the treatment planning facility.

27. The image generation method as set forth in any one of claims 24-25, wherein the selecting one or more parameters for a re-reconstruction includes selecting one or more parameters from a group consisting of: spatial resolution or voxel size; a selection of a sub-set of the image data (30, 160) for the re-reconstruction; and a selection to perform local motion compensation of at least some image data prior to the re-reconstruction.