Motion artifact prediction during data acquisition

A non-transitory computer readable medium storing machine executable instructions for execution by a processor controlling a magnetic resonance imaging system using pulse sequence commands and a first machine learning model comprising a first deep learning network, wherein the pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire a set of magnetic resonance imaging data, wherein the first machine learning model comprises a first input and a first output,

• wherein execution of the machine executable instructions causes the processor to repeatedly perform an acquisition and analysis process, wherein for each current repetition, the acquisition and analysis process comprises:
• acquiring a current dataset comprising a subset of the set of magnetic resonance imaging data from an imaging zone of the magnetic resonance imaging system according to the pulse sequence commands;
• providing the current dataset to the first input of the first machine learning model in combination with at least a previous dataset acquired in a previous repetition preceding the current repetition;
• receiving a prediction of a motion artifact level of the acquired magnetic resonance imaging data from the first output of the first machine learning model, the motion artifact level characterizing a number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data;
• when the prediction indicates an increased number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data relative to a previous prediction received in the previous repetition preceding the current repetition, checking whether omitting the current repetition results in a next prediction received in a next repetition following the current repetition that also indicates an increased number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data relative to the previous prediction of the previous repetition preceding the current repetition;
• when omitting the current repetition does not result in the next prediction indicating an increased number and/or extent of motion artifacts, continuing the repeated performing of the acquisition and analysis process without the current dataset;
• and
• when omitting the current repetition does result in the next prediction indicating an increased number and/or extent of motion artifacts, continuing the repeated performing with the current dataset.

A method of operating a magnetic resonance imaging system, the method comprising:

• providing pulse sequence commands for controlling the magnetic resonance imaging system to acquire a set of magnetic resonance imaging data;
• providing a first machine learning model comprising a first deep learning network, wherein the first machine learning model comprises a first input and a first output;
• and
• repeatedly performing an acquisition and analysis process, wherein for each current repetition, the acquisition and analysis process comprises:
• acquiring a current dataset comprising a subset of the set of magnetic resonance imaging data from an imaging zone of the magnetic resonance imaging system according to the pulse sequence commands;
• providing the current dataset to the first input of the first machine learning model in combination with at least a previous dataset acquired in a previous repetition preceding the current repetition;
• receiving a prediction of a motion artifact level of the acquired magnetic resonance imaging data from the first output of the first machine learning model, the motion artifact level characterizing a number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data;
• when the prediction indicates an increased number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data relative to a previous prediction received in the previous repetition preceding the current repetition, checking whether omitting the current repetition results in a next prediction received in in a next repetition following the current repetition that also indicates an increased number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data relative to the prediction of the previous repetition preceding the current repetition;
• when omitting the current repetition does not result in the next prediction indicating an increased number and/or extent of motion artifacts, continuing the repeated performing of the acquisition and analysis process without the current dataset;
• and
• when omitting the current repetition does not in the next prediction indicating an increased number and/or extent of motion artifacts, continuing the repeated performing of the acquisition and analysis process with the current dataset.