Iterative hierarchal network for regulating medical image reconstruction
AssigneeSiemens Healthineers AG
InventorsMahmoud Mostapha, Boris Mailhe, Mariappan S. Nadar, Simon Arberet, Marcel Dominik Nickel
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Abstract
For reconstruction in medical imaging, such as reconstruction in MR imaging, an iterative, hierarchal network for regularization may decrease computational complexity. The machine-learned network of the regularizer is unrolled or made iterative. For each iteration, nested U-blocks form a hierarchy so that some of the down-sampling and up-sampling of some U-blocks begin and end with lower resolution data or features, reducing computational complexity.
Claims20
A method for reconstruction of a medical image in a medical imaging system, the method comprising:
- scanning, by the medical imaging system, a patient, the scanning resulting in measurements;
- reconstructing, by an image processor, the medical image from the measurements, the reconstructing including a regularizer implemented with a machine-learned network comprising iterative hierarchal convolutional networks, each of the iterative hierarchal convolutional networks including both down-sampling and up-sampling nested within other down-sampling and up-sampling;
- and
- displaying the medical image.
A method of machine training for reconstruction in medical imaging, the method comprising:
- machine training a neural network with an unrolled arrangement of U-blocks in a sequence as a regularizer for the reconstruction in the medical imaging, the neural network comprising some of the U-blocks nested within other of the U-blocks;
- and
- storing the neural network as machine trained.
A system for reconstruction in medical imaging, the system comprising:
- a medical scanner configured to scan a region of a patient, the scan providing scan data;
- an image processor configured to reconstruct a representation of the region from the scan data, the image processor configured to reconstruct by application of a machine-learned model in a regularization stage, the machine-learned model comprising a down-sampling and up-sampling first block having multiple down-sampling and up-sampling second blocks;
- and
- a display configured to display an image of the region from the reconstructed representation.
Dependencies are inferred from the claim text.