Realtime design of radio-frequency pulses and gradient pulses in magnetic resonance imaging

1. A medical system comprising:

a non-transitory memory configured to store machine executable instructions and a convolutional neural network, wherein the convolutional neural network is configured to receive as input a complex array encoding a selection of at least one excitation field of view, wherein the convolutional neural network is configured to output a radio frequency waveform and multiple spatially selective gradient pulse waveforms, wherein the convolutional neural network is a multi-task convolutional neural network with a first output for the radio frequency waveform and a separate output for each of the multiple spatially selective gradient pulse waveforms; and

a computational system, wherein execution of the machine executable instructions causes the computational system to:

receive the selection of the at least one excitation field of view;

receive initial pulse sequence commands configured for controlling a magnetic resonance imaging system to acquire k-space data descriptive of a subject;

encode the complex array using the at least one excitation field of view;

receive the radio frequency waveform and the multiple spatially selective gradient pulse waveforms in response to inputting the complex array into the convolutional neural network; and

construct modified pulse sequence commands by modifying the initial pulse sequence commands with the radio frequency waveform and the multiple spatially selective gradient pulse waveforms such that the pulse sequence commands are configured to control the magnetic resonance imaging system to acquire the k-space data from the at least one excitation field of view.

2. A computer program comprising machine executable instructions stored on a non-transitory computer readable medium for execution by a computational system controlling a medical system, wherein the computer program further comprises: a convolutional neural network configured to receive as input a complex array encoding a selection of at least one excitation field of view, wherein the convolutional neural network is configured to output a radio frequency waveform and multiple spatially selective gradient pulse waveforms, wherein the convolutional neural network is a multi-task convolutional neural network with a first output for the radio frequency waveform and a separate output for each of the multiple spatially selective gradient pulse waveforms, wherein execution of the machine executable instructions causes the computational system to:

receive the selection of the at least one excitation field of view;

receive initial pulse sequence commands configured for controlling a magnetic resonance imaging system to acquire k-space data descriptive of subject;

encode the complex array using the at least one excitation field of view;

receive the radio frequency waveform and the multiple spatially selective gradient pulse waveforms in response to inputting the complex array into the convolutional neural network; and

construct modified pulse sequence commands by modifying the initial pulse sequence commands with the radio frequency waveform and the multiple spatially selective gradient pulse waveforms such that the pulse sequence commands are configured to control the magnetic resonance imaging system to acquire the k-space data from the at least one excitation field of view.

3. A method of medical imaging using a convolutional neural network for executution by a computational system, wherein the convolutional neural network is configured to receive as input a complex array encoding a selection of at least one excitation field of view, wherein the convolutional neural network is configured to output a radio frequency waveform and multiple spatially selective gradient pulse waveforms, wherein the convolutional neural network is a multi-task convolutional neural network with a first output for the radio frequency waveform and a separate output for each of the multiple spatially selective gradient pulse waveforms, wherein the method comprises:

receiving, by the computational system, the selection of the at least one excitation field of view;

receiving, by the computational system, initial pulse sequence commands configured for controlling a magnetic resonance imaging (MRI) system to acquire k-space data descriptive of subject-;

encoding, by the computational system, the complex array using the at least one excitation field of view;

receiving, by the computational system, the radio frequency waveform and the multiple spatially selective gradient pulse waveforms in response to inputting the complex array into the convolutional neural network; and

constructing, by the computational system, modified pulse sequence commands by modifying the initial pulse sequence commands with the radio frequency waveform and the multiple spatially selective gradient pulse waveforms such that the pulse sequence commands are configured to control the magnetic resonance imaging system to acquire the k-space data from the at least one excitation field of view.

4. A method of training a convolutional neural network by a computational system, wherein the method comprises repeatedly:

generating a training radio frequency waveform and multiple training spatially selective gradient pulse waveforms using a selective excitation pulse design algorithm, by the computational system;

calculating a modeled excitation field of view, by the computational system, by inputting the training radio frequency waveform and training multiple spatially selective gradient pulse waveforms into a magnetic resonance imaging signal model;

receiving a forward propagated radio frequency waveform and multiple forward propagated spatially selective training gradient pulse waveforms, by the computational system, by inputting the modeled excitation field of view into the convolutional neural network;

updating parameters of the convolutional neural network, by the computational system, by performing back propagation with the training radio frequency waveform and the forward propagated radio frequency waveform and with matching pairs of the multiple training spatially selective gradient pulse waveforms and the multiple forward propagated spatially selective training gradient pulse waveforms.

5. The medical system of claim 1 , wherein the convolutional neural network is trained by the computational system repeatedly performing the following steps:

generating a training radio frequency waveform and multiple training spatially selective gradient pulse waveforms using a selective excitation pulse design algorithm;

calculate a modeled excitation field of view by inputting the training radio frequency wave form and training multiple spatially selective gradient pulse waveforms into a magnetic resonance imaging signal model;

receive a forward propagated radio frequency waveform and multiple forward propagated spatially selective training gradient pulse waveforms by inputting the modeled excitation field of view into the convolutional neural network;

update parameters of the convolutional neural network by performing back propagation with the training radio frequency wave form and the forward propagated radio frequency waveform and with matching pairs of the multiple training spatially selective gradient pulse waveforms and the multiple forward propagated spatially selective training gradient pulse waveforms.

6. The medical system of claim 1 , wherein the pulse sequence commands are configured for acquiring the k-space data according to a parallel imaging magnetic resonance imaging protocol.

7. The medical system of claim 1 , wherein execution of the machine executable instructions further causes the computational system to:

receive a survey magnetic resonance image;

render the survey magnetic resonance image on a display; and

receive the selection of the at least one excitation field of view in response to displaying the survey magnetic resonance image, wherein the selection of the at least one excitation field of view is within the survey magnetic resonance image.

8. The medical system of claim 1 , wherein the selection of the at least one field of view is received from any one of the following: an automated image segmentation algorithm, from a user interface, and combinations thereof.

9. The medical system of claim 1 , wherein the multiple spatially selective gradient pulse waveforms are two spatially selective gradient pulse waveforms, and wherein the each of at least one excitation field of view is a two-dimensional excitation field of view.

10. The medical system of claim 1 , wherein the multiple spatially selective gradient pulse waveforms are three spatially selective gradient pulse waveforms, and wherein the each of at least one excitation field of view is a three-dimensional excitation field of view.

11. The medical system of claim 1 , wherein each of the at least one excitation field of view have an arbitrary shape and/or are disconnected spatially.

12. The medical system of claim 1 , wherein the convolutional neural network is any one of the following: a multi-scale convolutional neural network or a U-net convolutional neural network.

13. The medical system of claim 1 , wherein the medical system comprises the magnetic resonance imaging system, wherein execution of the machine executable instructions further causes the computational system to:

acquire the k-space data by controlling the magnetic resonance imaging system with the modified pulse sequence commands; and

reconstruct magnetic resonance imaging data from the k-space data.

14. The medical system of claim 5 , wherein the selective excitation pulse design algorithm is at least one of the following: a Shinnar-Le Roux algorithm, a small-flip-angle approximation algorithm, or a numerical optimal control algorithm.

15. The medical system of claim 5 , wherein the magnetic resonance imaging signal model is a numerical solution of the Bloch equations.