Time compressed dynamic MR deep learning reconstruction
AssigneeSiemens Healthineers AG
InventorsSimon Arberet, Mahmoud Mostapha, Marcel Dominik Nickel, Mariappan S. Nadar
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Abstract
Systems and methods for reconstruction for a medical imaging system. Non-Cartesian k-space data is acquired using a dynamic MR sequence. A time compression network compresses the non-Cartesian data. The compressed data is used for reconstruction of an image. The time compression network is configured to reduce the (time and memory) complexity of the reconstruction process.
Claims20
A method of reconstruction for a medical imaging system, the method comprising:
- scanning a patient by the medical imaging system, the scanning acquiring k-space scan data using a dynamic MR sequence that includes at least a time component;
- applying, by a time compression network trained jointly with an unrolled iterative reconstruction network, a learned temporal compression to the k-space scan data to generate a time compression matrix that represents temporal correlations of the dynamic MR sequence;
- reconstructing an image using the unrolled iterative reconstruction network that includes at least a data-consistency step, wherein the time compression matrix is used within the data-consistency step;
- and
- outputting the image.
A system for time compressed dynamic magnetic resonance deep learning reconstruction, the system comprising:
- a medical imaging system configured to acquire k-space scan data using a non-Cartesian dynamic sequence;
- a time compression network configured to compress the k-space scan data;
- and
- an unrolled iterative reconstruction network trained jointly with the unrolled iterative reconstruction network, the unrolled iterative reconstruction network configured to reconstruct an image, wherein the unrolled iterative reconstruction network is configured to apply, using the time compression network, a learned temporal compression to the k-space scan data to generate a time compression matrix that represents temporal correlations of the non-Cartesian dynamic sequence, wherein the unrolled iterative reconstruction network includes at least a data-consistency step, wherein the time compression matrix is used within the data-consistency step.
A non-transitory computer readable storage medium comprising a set of computer-readable instructions stored thereon which, when executed by at least one processor cause the processor to:
- acquire non-Cartesian k-space scan data that includes at least a time component;
- apply, by a time compression network trained jointly with an unrolled iterative reconstruction network, a learned temporal compression to the non-Cartesian k-space scan data to generate a time compression matrix that represents temporal correlations of the non-Cartesian k-space scan data;
- reconstruct an image using an unrolled iterative reconstruction that includes at least a data-consistency step, wherein the time compression matrix is used within the data-consistency step;
- and
- output the image.
Dependencies are inferred from the claim text.