Generating setting parameters for an MRI sequence

A computer-implemented method for establishing a system for generating setting parameters for a magnetic resonance imaging (MRI) sequence to be performed in accordance with an MRI scanner, the method comprising:

• operating an MRI scanner to generate training data comprising input data relating to the MRI sequence to be performed, the input data having a data structure corresponding to an input vector including corresponding setting parameters as labels and indicating a percentage of one or more types of MRI sequences to be performed;
• operating the MRI scanner to generate a conditional generative artificial neural network and a conditional discriminative artificial neural network;
• operating the MRI scanner to train the conditional generative artificial neural network using the training data, the conditional generative artificial neural network being trained such that the setting parameters for MRI sequences to be performed are generated based on the input data, the conditional generative artificial neural network being provided with conditions to be fulfilled with respect to the percentage of the one or more types of MRI sequences to be performed such that setting parameters are generated for, respectively, at least one T1-weighted MRI sequence, one T2-weighted MRI sequence, and one proton-density-weighted MRI sequence,
• wherein the conditions to be fulfilled are provided to the conditional generative artificial neural network as a concatenation of a condition vector with the input vector, and
• wherein the condition vector has a data structure comprising non-zero entries that respectively indicate a percentage of the one or more types of MRI sequences to be performed;
• operating the MRI scanner to train the conditional discriminative artificial neural network using the training data and the setting parameters generated by the conditional generative artificial neural network, the trained conditional discriminative artificial neural network being configured to discriminate, for each respective one of the setting parameters from among the generated setting parameters, whether each setting parameter is associated with the training data or is associated with the setting parameters generated by the conditional generative artificial neural network, the conditional generative artificial neural network being further trained until the conditional discriminative artificial neural network is unable to discriminate the training data from the setting parameters generated by the conditional generative artificial neural network;
• and
• operating the MRI scanner to perform MRI sequences in accordance with the received input data and the setting parameters generated using the trained conditional generative artificial neural network.

A computer-implemented method for establishing a system for generating setting parameters for a magnetic resonance imaging (MRI) sequence to be performed in accordance with an MRI scanner, the method comprising:

• operating an MRI scanner to receive input data in the form of a data structure corresponding to an input vector indicating a percentage of one or more types of MRI sequences to be performed;
• and
• operating an MRI scanner to generate, using a conditional generative artificial neural network that has been trained as part of a conditional generative adversarial net, setting parameters for the MRI sequence to be performed based on the received input data,
• wherein the conditional generative adversarial net comprises the conditional generative artificial neural network as a generative part and a conditional discriminative artificial neural network as a discriminative part;
• and
• wherein the conditional generative artificial neural network is provided with conditions to be fulfilled with respect to the percentage of the one or more types of MRI sequences to be performed such that setting parameters are generated for, respectively, at least one T1-weighted MRI sequence, one T2-weighted MRI sequence, and one proton-density-weighted MRI sequence,
• wherein the conditions to be fulfilled are provided to the conditional generative artificial neural network as a concatenation of a condition vector with the input vector, and
• wherein the condition vector has a data structure comprising non-zero entries that respectively indicate a percentage of the one or more types of MRI sequences to be performed.

A non-transitory computer readable medium loaded onto a control computer of a magnetic resonance imaging (MRI) scanner for generating setting parameters for a magnetic resonance imaging (MRI) sequence to be performed, the non-transitory computer readable medium having instructions stored thereon that, when executed by the control computer, cause the MRI scanner to:

• receive input data in the form of a data structure corresponding to an input vector indicating a percentage of one or more types of MRI sequences to be performed;
• and
• generate, using a trained conditional generative artificial neural network that has been trained as part of a conditional generative adversarial net, setting parameters for the MRI sequence to be performed based on the received input data,
• wherein the conditional generative adversarial net comprises the conditional generative artificial neural network as a generative part and a conditional discriminative artificial neural network as a discriminative part;
• and
• wherein the conditional generative artificial neural network is provided with conditions to be fulfilled with respect to the percentage of the one or more types of MRI sequences to be performed such that setting parameters are generated for, respectively, at least one T1-weighted MRI sequence, one T2-weighted MRI sequence, and one proton-density-weighted MRI sequence,
• wherein the conditions to be fulfilled are provided to the conditional generative artificial neural network as a concatenation of a condition vector with the input vector, and
• wherein the condition vector has a data structure comprising non-zero entries that respectively indicate a percentage of the one or more types of MRI sequences to be performed.