Fast statistical imaging reconstruction via denoised ordered-subset statistically-penalized algebraic reconstruction technique
AssigneeWisconsin Alumni Research Foundation
InventorsGuang-Hong Chen, Jie Tang
Life of this patent
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Abstract
Described here are systems and methods for iteratively reconstructing images from data acquired using a medical imaging system. The image reconstruction is decomposed into separate linear sub-problems that can be more efficiently solved. A statistical image reconstruction process is decomposed into a statistically-weighted algebraic reconstruction update sequence. After this step, the reconstructed image is denoised using a regularization function.
Claims19
A method for reconstructing an image of a subject using a medical imaging system, the steps of the method comprising:
- a) acquiring data from the subject using the medical imaging system;
- b) reconstructing an image of the subject from the acquired data using an iterative statistical image reconstruction that is decomposed to include in each iteration an image reconstruction step without regularization and a denoising step that includes regularization.
A method for reconstructing an image of a subject using a medical imaging system, the steps of the method comprising:
- a) acquiring data from the subject using the medical imaging system;
- b) reconstructing an image of the subject from the acquired data by iteratively minimizing a cost function such that during each iteration an estimate of the image is updated using a step value that is calculated by weighting a derivative of the cost function by a matrix that accounts for noise in the acquired data.
A method for reconstructing an image of a subject, the steps of the method comprising:
- acquiring data from a subject using a medical imaging system;
- and
- iteratively reconstructing an image of the subject from the acquired data by:
- i) establishing a cost function to minimize;
- ii) selecting an estimate of the image;
- iii) evaluating the cost function for the estimate;
- iv) producing an updated estimate by adding a step value to the estimate, the step value being calculated by:
- producing synthesized data by applying a system matrix to the estimate;
- producing difference data by calculating a difference between the synthesized data and the acquired data;
- producing noise-weighted data by applying a noise-weighting matrix to the difference data, the noise weighting matrix including an estimate of noise;
- producing the step value by applying a transpose of the system matrix to the noise-weighted data;
- v) evaluating a stopping criterion;
- and
- vi) storing the updated estimate as the image of the subject when the stopping criterion is satisfied and when the stopping criterion is not satisfied, storing the updated estimate as the estimate and repeating steps iii)-v).
Dependencies are inferred from the claim text.