General Electric Company

Koninklijke Philips Corporation N.v.

Manivannan Jayapalan

Jaganathan Vellagoundar

Ashok Kumar P Kumar Reddy

Disclosed herein is a magnetic resonance imaging system () controlled by a processor (). Execution of machine executable instructions causes the processor to receive a selection input of gradient coil pulse commands, to provide the selected commands and at least one value relating to a further parameter to a trained machine learning system (), to receive from the machine learning system information as to anti-noise to be generated by a sound transducer () to compensate for noise experienced at the ears of a subject () in the magnetic resonance imaging system. The machine executable instructions further cause the processor to control the magnetic resonance imaging system with the pulse sequence commands and the set of gradient coil pulse commands for acquisition of the imaging k-space data and to synchronized therewith operate the sound transducer for generating anti-noise using the information as output by the trained machine learning system.

Magnetic resonance imaging system generating anti-noise

Apparatus, systems, and methods for improved imaging device configuration are disclosed and described. An example apparatus includes a memory storing a first neural network that is trained to map image quality metrics to corresponding scan parameters. The example apparatus includes a processor configured to: receive specified image quality metrics; instruct the trained first neural network to generate scan parameters based on the specified image quality metrics to configure an imaging device for image acquisition; and instruct the imaging device to acquire one or more resulting images using the generated scan parameters.

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Bengaluru, India

Manivannan Jayapalan