Information propagation in prior-image-based reconstruction
A framework, comprising techniques, process(es), device(s), system(s), combinations thereof, or the like, to analyze propagation of information in prior-image-based reconstruction by decomposing the estimation into distinct components supported by a current data acquisition and by a prior image. Such decomposition can quantify contributions from prior data and current data as a spatial map and/or can trace specific features in an image to a source of at least some of such features.
1. A method for evaluation of propagation of information in prior-image-based reconstruction, the method comprising:
providing an objective function, wherein the objective function comprises a second term representing a generalized image penalty that discourages roughness in the reconstruction through use of one selected from a group consisting of a gradient applied to an image volume, a sparsifying operator applied to an image volume, a combination thereof, or a p-norm metric; and
decomposing the objective function into a first component dependent at least on current imaging data and a second component dependent at least on prior imaging data.
2. The method of claim 1 , wherein the decomposing step comprises approximating at least a portion of the objective function.
3. The method of claim 1 , wherein the objective function comprises a first term representing a log-likelihood function enforcing a fit between an attenuation estimate and the current imaging data.
4. The method of claim 3 , wherein the decomposing step comprises approximating the log-likelihood function.
5. The method of claim 3 , wherein the first term incorporates the relative data fidelity of different measurements.
6. The method of claim 1 , wherein the p-norm metric is a quadratic penalty.
7. The method of claim 1 , wherein the objective function comprises a second term representing a generalized image penalty that discourages roughness in the reconstruction through a p-norm metric and use of a gradient applied to an image volume, a sparsifying operator applied to an image volume, or a combination thereof.
8. A system, comprising:
a memory having computer-executable instructions encoded thereon; and
a processor functionally coupled to the memory and configured, by the computer-executable instructions, to provide an objective function, wherein the objective function comprises a second term representing a generalized image penalty that discourages roughness in the reconstruction through use of one selected from a group consisting of a gradient applied to an image volume, a sparsifying operator applied to an image volume, a combination thereof, or a p-norm metric; and
to decompose the objective function into a first component dependent at least on current imaging data and a second component dependent at least on prior imaging data.
9. The system of claim 8 , wherein the decomposing step comprises approximating at least a portion of the objective function.
10. The system of claim 8 , wherein the objective function comprises a first term representing a log-likelihood function enforcing a fit between an attenuation estimate and the current imaging data.
11. The system of claim 10 , wherein the processor is further configured to generate an approximation of the log-likelihood function.
12. The system of claim 10 , wherein the first term incorporates the relative data fidelity of different measurements.
13. The system of claim 8 , wherein the p-norm metric is a quadratic penalty.
14. The system of claim 8 , wherein the objective function comprises a second term representing a generalized image penalty that discourages roughness in the reconstruction through a p-norm metric and use of a gradient applied to an image volume.
15. The system of claim 8 , wherein the objective function comprises a second term representing a generalized image penalty that discourages roughness in the reconstruction through a p-norm metric and use of a sparsifying operator applied to an image volume.
16. A method for quantification of propagation of information in prior-image-based reconstruction, the method comprising:
providing an objective function, wherein the objective function comprises a second term representing a generalized image penalty that discourages roughness in the reconstruction through use of one selected from a group consisting of a gradient applied to an image volume, a sparsifying operator applied to an image volume, a combination thereof, or a p-norm metric;
decomposing the objective function into a first component dependent at least on current imaging data and a second component dependent at least on prior imaging data;
evaluating the first component;
evaluating the second component; and
quantifying a contribution of a prior image to a current image based at least on the first component and the second component, wherein the current image is obtained at least in part from the current imaging data, and wherein the prior image is obtained at least in part from the prior imaging data.