Quasi-Newton MRI deep learning reconstruction
AssigneeSiemens Healthineers AG
InventorsSimon Arberet, Marcel Dominik Nickel
Life of this patent
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Abstract
Systems and methods reconstruction for a medical imaging system using a quasi-newton method. An unrolled iterative reconstruction process is used to reconstruct an image from the scan data. The unrolled iterative reconstruction process includes a plurality of cascades that include at least a data-consistency step and a regularization step. The data-consistency step is modified based at least in part on information of already calculated gradients of one or more previous cascades of the plurality of cascades using a quasi-newton computation.
Claims13
A system for reconstruction of a magnetic resonance image, the system comprising:
- a medical imaging scanner configured to acquire under sampled k-space data of a patient;
- and
- an image processor configured to reconstruct and output the magnetic resonance image from the under sampled k-space data using an unrolled iterative reconstruction process comprising an inverse problem, wherein each iteration of the unrolled iterative reconstruction process comprises a data-consistency step and a regularization step performed by a trained neural network, wherein the image processor is configured to compute the data-consistency step, for each cascade, using a quasi-Newton preconditioner that is dynamically adapted to a locally trained data-consistency loss of a current cascade by an:
- initialization of the quasi-Newton preconditioner using a direct sensitivity encoding (SENSE) pseudo-inverse matrix;
- an update of the quasi-Newton preconditioner using a limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm with gradients and inputs retained from previous cascades;
- and
- an application of the updated quasi-Newton preconditioner to a gradient direction that is weighted in the k-space domain by a learned density-compensation function.
A method for MR image reconstruction, the method comprising:
- acquiring, by a medical imaging scanner, under sampled k-space data acquired using a general pattern;
- reconstructing the MR image from the under sampled k-space data using an unrolled iterative reconstruction comprising an inverse problem, wherein each iteration of the unrolled iterative reconstruction comprises a data-consistency step and a regularization step performed by a trained neural network, wherein the data-consistency step is computed, for each cascade, using a quasi-Newton preconditioner that is dynamically adapted to a locally trained data-consistency loss of the current cascade by:
- initializing of the quasi-Newton preconditioner using a direct sensitivity encoding (SENSE) pseudo-inverse matrix;
- updating the quasi-Newton preconditioner using a limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm with gradients and inputs retained from previous cascades;
- and
- applying the updated quasi-Newton preconditioner to a gradient direction that is weighted in the k-space domain by a learned density-compensation function;
- and
- outputting the reconstructed MR image.
A method for MR image reconstruction, the method comprising:
- acquiring, by a medical imaging scanner, under sampled k-space data acquired using a regular sampling pattern;
- reconstructing the MR image using an unrolled iterative reconstruction that is formulated as an inverse problem, wherein each iteration of the unrolled iterative reconstruction comprises a data-consistency step and a regularization step performed by a trained neural network, wherein the data-consistency step is computed, for each cascade, using a quasi-Newton preconditioner that is dynamically adapted to a locally trained data-consistency loss of the current cascade by:
- initializing of the quasi-Newton preconditioner using a direct sensitivity encoding (SENSE) pseudo-inverse matrix;
- updating the quasi-Newton preconditioner using a limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm with gradients and inputs retained from previous cascades;
- and
- applying the updated quasi-Newton preconditioner to a gradient direction that is weighted in the k-space domain by a learned density-compensation function;
- and
- outputting the reconstructed MR image.
Dependencies are inferred from the claim text.