Model-based reconstruction for grasp MRI
AssigneeSiemens Healthineers AG
InventorsMahmoud Mostapha, Simon Arberet, Marcel Dominik Nickel, Mariappan S. Nadar
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Abstract
Systems and methods for a deep learning reconstruction network with computationally light and efficient CNN architecture and a training strategy tailored to image reconstruction of dynamic multi-coil GRASP MRI. The configuration of the size of the network used in training time may be adjusted, which allows for higher accelerations and different hardware constraints.
Claims20
A method for reconstructing non-cartesian magnetic resonance imaging (MRI) data, the method comprising:
- training a convolutional neural network (CNN) on image pairs to minimize a loss;
- selecting a number of iterations for a reconstruction network;
- assembling the reconstruction network with the number of iterations of the CNN, each iteration further including a data consistency step, wherein the CNN of different iterations share the same weights;
- training the reconstruction network end to end;
- and
- applying the reconstruction network to non-cartesian MRI data of a patient acquired from a medical imaging procedure.
A system for magnetic resonance imaging (MRI) reconstruction, the system comprising:
- an MR imaging device configured to acquire non cartesian MRI data of a patient;
- a reconstruction network configured to input the non cartesian MRI data and output a representation, the reconstruction network comprising a selected number of iterations, wherein each iteration includes a convolutional neural network (CNN) and a data consistency step, wherein the reconstruction network is trained in two stages, wherein in a first stage of the two stages a single iteration of the CNN is trained, wherein in a second stage of the two stages, the reconstruction network comprising the selected number of iterations is trained end to end wherein weights for the CNN in each iteration are initialized with weights learned in the first stage;
- and
- a display configured to display the representation.
A method for MRI reconstruction, the method comprising:
- acquiring non cartesian MRI data of a patient;
- reconstructing a representation of the patient using a reconstruction network configured to input the non cartesian MRI data and output a representation, the reconstruction network comprising a selected number of iterations, wherein each iteration includes a convolutional neural network (CNN) and a data consistency step, wherein the reconstruction network is trained in two stages, wherein in a first stage of the two stages a single iteration of the CNN is trained, wherein in a second stage of the two stages, the reconstruction network comprising the selected number of iterations is trained end to end wherein weights for the CNN in each iteration are initialized with weights learned in the first stage;
- and
- displaying the representation of the patient.
Dependencies are inferred from the claim text.