Methods and systems for tomographic reconstruction
A method for processing an image of a series of images includes receiving first data representing a first previously reconstructed image and receiving second data representing a second image. A second image is reconstructed in accordance with the first data, the second data and a noise model. The noise model is a likelihood estimation. The second image is reconstructed in accordance with a penalty function. The penalty function is a roughness penalty function. The penalty function is updated by iteratively adjusting an image volume estimate. The penalty function is updated by iteratively adjusting a registration term. The penalty function is a prior image penalty function and the prior image penalty function and a registration term are jointly optimized. The penalty function is determined in accordance with a noise model. The function is a p-norm penalty function.
1. A method for processing an image of a series of images, comprising:
receiving first data representing a first previously reconstructed image of an object;
receiving, from an imaging device, second data representing a second image of the object, wherein the first data represents the object at a first time and the second data represents the object at a second time subsequent to the first time; and
reconstructing the second image in accordance with the first data, the second data and a measurement noise model, wherein the measurement noise model is configured to model noise in the second data introduced by the imaging device, and wherein reconstructing the second image accounts for time-based differences between the first data and the second data.
2. The method for processing an image of claim 1 , wherein the measurement noise model comprises a likelihood estimation.
3. The method for processing an image of claim 1 , further comprising reconstructing the second image in accordance with a penalty function configured to penalize a feature in reconstructing the second image.
4. The method for processing an image of claim 3 , wherein the penalty function comprises a roughness penalty function configured to penalize roughness or noise in reconstructing the second image.
5. The method for processing an image of claim 3 , further comprising updating the penalty function by iteratively adjusting an image volume estimate.
6. The method for processing an image of claim 3 , further comprising updating the penalty function by iteratively adjusting a registration parameter of the penalty function, wherein the registration parameter is configured to indicate a prior image for use in reconstructing the second image.
7. The method for processing data of claim 3 , wherein the penalty function is a prior image penalty function, and further comprising jointly optimizing the prior image penalty function and a registration parameter configured to indicate a prior image for use in reconstructing the second image.
8. The method of claim 1 , wherein the second image is reconstructed by iteratively optimizing an objective function, and wherein the objective function comprises a first term indicative of the measurement noise model and a second term configured to discourage time-based differences between the first previously reconstructed image and the second image.
9. The method for processing an image of claim 3 , wherein the penalty function comprises a p-norm penalty function.
10. The method for processing an image of claim 1 , further comprising reconstructing the second image in accordance with a plurality of penalty functions of an objective function, wherein reconstructing the second image comprises iteratively adjusting the plurality of penalty functions to optimize the objective function.
11. The method for processing an image of claim 10 , wherein the plurality of penalty functions comprises a first penalty function determined in accordance with the first data and a second penalty function determined in accordance with a roughness function of the second data, wherein the roughness function is configured to penalize roughness or noise in reconstructing the second image.
12. The method for processing an image of claim 11 , wherein the first penalty function comprises a penalty function determined in accordance with a difference between the first data and the second data.
13. The method for processing an image of claim 1 , further comprising registering the first and second images in accordance with a registration parameter and an image parameter, wherein the registration parameter and the image parameters are parameters of a penalty term of an objective function solved in reconstructing the second image.
14. The method for processing an image of claim 13 , wherein the image parameter is an image volume.
15. The method for processing an image of claim 13 , further comprising jointly optimizing the registration parameter and an image parameter.
16. The method for processing an image of claim 15 , wherein jointly optimizing the registration parameter and the image parameter further comprises: (a) performing an optimization over the registration parameter with the image parameter fixed; (b) performing an optimization over the image parameter with the registration parameter fixed; and (c) repeating steps (a) and (b) until an objective function is maximized.
17. The method for processing data of claim 1 , further comprising registering the first image and the second image jointly with reconstructing the second image.
18. The method for processing data of claim 17 , further comprising registering the first image and the second image jointly with reconstructing the second image by adjusting a registration parameter and an image volume estimate, wherein the registration parameter and the image volume estimate are terms of an objective function solved in reconstructing the second image.
19. A system for processing an image of a series of images, comprising:
a memory having encoded thereon computer-executable instructions; and
a processor functionally coupled to the memory and configured, by the computer-executable instructions, to perform at least the following actions,
receiving first data representing a first previously reconstructed image of an object,
receiving, from an imaging device, second data representing a second image of the object, wherein the first data represents the object at a first time and the second data represents the object at a second time subsequent to the first time, and
reconstructing a second image provided in accordance with the first data, the second data and a measurement noise model, wherein the measurement noise model is configured to model noise in the second data introduced by the imaging device, and wherein reconstructing the second image accounts for time-based differences between the first data and the second data.
20. The system for processing an image of claim 19 , wherein the second image is reconstructed in accordance with a likelihood.
21. The system for processing an image of claim 19 , wherein the second image is reconstructed jointly with an adjustment of an image registration parameter configured to indicate a prior image for use in reconstructing the second image.