Methods and systems for automatic control of subjective image quality in imaging of objects

A control system for automatic control of image quality in imaging of at least one object using an imaging system including signal generator for generating and transmitting image generating signals into the object using selected system parameter sets of the imaging system, a signal receiver for receiving sets of signals from the object and an image processor configured to process the sets of signals into images, wherein:

• the signal generator/signal receiver is configured to transmit/receive image signals into the at least one object/from the at least one object using preferred sets of system parameters selected according to at least one subjective expert opinion;
• the image processor is configured to process the received signals into a number of images reflecting the subjective expert opinion;
• a training data generator is configured to generate sets of image variations for training based on different sets of system parameters;
• and
• a neural network is configured to:
• process data representing the images reflecting the subjective expert opinion and the image variations for training,
• for each set of image variations for training, auto-identify image features that correlate to image features of the images reflecting the subjective expert opinion, and
• create a trained network comprising the auto-identified image features, wherein the trained network is configured to use the auto-identified image features to provide corrections for system parameters, said corrections predicting how to modify system parameters to obtain system parameters reflecting desired subjective expert image preferences.

A control method for automatic control of image quality in ultrasound imaging of at least one object using an ultrasound system including a transmitter/receiver for transmitting/receiving ultrasound image signals into the at least one object/from the at least one object using system parameter sets of the ultrasound system, and an ultrasound image processor configured to process the sets of signals into ultrasound images, wherein:

• the transmitter/receiver is configured to transmit/receive ultrasound image signals into the at least one object/from the at least one object using preferred sets of system parameters selected according to at least one subjective expert opinion;
• the ultrasound image processor is configured to process the received signals into a number of ultrasound images reflecting the subjective expert opinion;
• generating, in a training data generator, sets of image variations for training based on different sets of system parameters;
• and, in a neural network:
• processing data representing the images reflecting the subjective expert opinion and the image variations for training,
• for each set of image variations for training, auto-identifying image features that correlate to image features of the ultrasound images reflecting the subjective expert opinion, and
• creating a trained network comprising the auto-identified image features, wherein the trained network is configured to use the auto-identified image features to provide corrections for system parameters, said corrections predicting how to modify system parameters to obtain system parameters reflecting desired subjective expert image preferences.

A control method for automatic control of image quality in imaging of at least one object using an imaging system including signal generator for generating and transmitting image generating signals into the object using selected system parameter sets of the imaging system, a signal receiver for receiving sets of signals from the object and an image processor configured to process the sets of signals into images, wherein:

• the signal generator/signal receiver is configured to transmit/receive image signals into the at least one object/from the at least one object using preferred sets of system parameters selected according to at least one subjective expert opinion;
• the image processor is configured to process the received signals into a number of images reflecting the subjective expert opinion;
• generating, in a training data generator, sets of image variations for training based on different sets of system parameters;
• and, in a neural network:
• processing data representing the images reflecting the subjective expert opinion and the image variations for training,
• for each set of image variations for training, auto-identifying image features that correlate to image features of the images reflecting the subjective expert opinion, and
• creating a trained network comprising the auto-identified image features, wherein the trained network is configured to use the auto-identified image features to provide corrections for system parameters, said corrections predicting how to modify system parameters to obtain system parameters reflecting desired subjective expert image preferences.