System and method for reconstructing an image
View Patent ↗The present disclosure relates to methods, systems, and non-transitory computer readable mediums for reconstructing an image. Image data may be obtained, wherein the image data may include projection data and may be generated by an imaging device. An objective function associated with a target image may be determined based on the image data. The objective function may include a difference model, wherein the difference model may represent a difference between a projection of the target image and the image data. The difference model may be determined based on a weighting matrix, and the weighting matrix may be determined based on an extended Tam window. The target image may be reconstructed by performing a plurality of iterations based on the objective function.
1. A method implemented on a computing device having at least one processor, at least one computer-readable storage medium, and a communication port connected to an imaging device, the method comprising:
obtaining image data, wherein the image data includes projection data and is generated by the imaging device;
determining an objective function associated with a target image based on the image data, the objective function including a difference model, wherein the difference model represents a difference between a projection of the target image and the image data, wherein the difference model is determined based on a weighting matrix, and the weighting matrix is determined based on an extended Tam window; and
reconstructing the target image by performing a plurality of iterations based on the objective function.
2. The method of claim 1 , wherein the extended Tam window is determined by extending a range of a Tam window.
3. The method of claim 2 , wherein the weighting matrix relates to the image data and includes a plurality of weighting factors that are within a range from 0 to 1.
4. The method of claim 3 , wherein:
the imaging device includes a detector array, the detector array includes a plurality of rows of detector units, the detector units are arranged in a row direction and a channel direction; and
the method further includes determining the plurality of weighting factors of the weighting matrix based on at least one parameter relating to the detector array, the at least one parameter including a position of at least one detector unit that detects the image data in the row direction.
5. The method of claim 4 , wherein the determining the plurality of weighting factors of the weighting matrix includes:
applying a first parameter and a second parameter to determine or adjust the plurality of weighting factors, the first parameter defining a first range of a position of a detector unit in the row direction corresponding to a first set of weighting factors that are equal to 1, and the second parameter defining a second range of a position of a detector unit in the row direction corresponding to a second set of weighting factors that are less than 1.
6. The method of claim 1 , wherein the objective function further includes a regularization item for denoising the image data, the regularization item including an adjustment model for adjusting an intensity of denoising the image data, wherein the second model is determined based on the weighting matrix, and the weighting matrix is determined based on the extended Tam window.
7. The method of claim 6 , wherein the second model relates to an intensity of denoising an image estimate generated in at least one of the plurality of iterations.
8. The method of claim 6 , wherein the second model is determined based further on a square of a back-projection of the image data weighted by the weighting matrix.
9. The method of claim 6 , wherein the objective function is a sum of the difference model and the regularization item.
10. The method of claim 1 , wherein the method further includes:
determining a constraint associated with the objective function for determining a target value of the objective function, wherein the target value corresponds to the target image.
11. The method of claim 1 , wherein the method further includes:
pre-processing the image data, wherein the pre-processing the image data includes denoising the image data based on a noise statistical model.
12. A system, comprising:
at least one non-transitory computer-readable storage medium including a set of instructions;
at least one processor in communication with the at least one non-transitory computer-readable storage medium, wherein when executing the instructions, the at least one processor is configured to cause the system to perform operations including:
obtaining image data, wherein the image data includes projection data and is generated by the imaging device;
determining an objective function associated with a target image based on the image data, the objective function including a difference model, wherein the difference model represents a difference between a projection of the target image and the image data, wherein the difference model is determined based on a weighting matrix, and the weighting matrix is determined based on an extended Tam window; and
reconstructing the target image by performing a plurality of iterations based on the objective function.
13. The system of claim 12 , wherein the extended Tam window is determined by extending a range of a Tam window.
14. The system of claim 13 , wherein the weighting matrix relates to the image data and includes a plurality of weighting factors that are within a range from 0 to 1.
15. The system of claim 14 , wherein:
the imaging device includes a detector array, the detector array includes a plurality of rows of detector units, the detector units are arranged in a row direction and a channel direction; and
the operations further include determining the plurality of weighting factors of the weighting matrix based on at least one parameter relating to the detector array, the at least one parameter including a position of at least one detector unit that detects the image data in the row direction.
16. The system of claim 15 , wherein the determining the plurality of weighting factors of the weighting matrix includes:
applying a first parameter and a second parameter to determine or adjust the plurality of weighting factors, the first parameter defining a first range of a position of a detector unit in the row direction corresponding to a first set of weighting factors that are equal to 1, and the second parameter defining a second range of a position of a detector unit in the row direction corresponding to a second set of weighting factors that are less than 1.
17. The system of claim 12 , wherein the objective function further includes a regularization item for denoising the image data, the regularization item including an adjustment model for adjusting an intensity of denoising the image data, wherein the second model is determined based on the weighting matrix, and the weighting matrix is determined based on the extended Tam window.
18. The system of claim 16 , wherein the objective function is a sum of the difference model and the regularization item.
19. The system of claim 12 , wherein the operations further include:
determining a constraint associated with the objective function for determining a target value of the objective function, wherein the target value corresponds to the target image.
20. A non-transitory computer readable medium embodying a computer program product, the computer program product comprising instructions configured to cause a computing device to:
obtain image data, wherein the image data includes projection data and is generated by the imaging device;
determine an objective function associated with a target image based on the image data, the objective function including a difference model, wherein the difference model represents a difference between a projection of the target image and the image data, wherein the difference model is determined based on a weighting matrix, and the weighting matrix is determined based on an extended Tam window; and
reconstruct the target image by performing a plurality of iterations based on the objective function.