MOTION ARTIFACT PREDICTION DURING DATA ACQUISITION

A magnetic resonance imaging system including a memory configured to store machine executable instructions, pulse sequence commands, and a first machine learning model including a first deep learning network. The pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire a set of magnetic resonance imaging data. The first machine learning model includes a first input and a first output, a processor, wherein execution of the machine executable instructions causes the processor to control the magnetic resonance imaging system to repeatedly perform an acquisition and analysis process including: acquiring a dataset including 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 dataset to the first input of the first machine learning model, in response to the providing, 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.