General Electric Company
MACHINE LEARNING IN AN IMAGING MODALITY SERVICE CONTEXT

Abstract:

The present approach relates to detection of image artifacts symptomatic of needed calibration and/or failing hardware with no or limited human intervention, such as using machine learning. Detection of image artifacts can occur as part of normal imaging system operation and/or as part of a quality assessment of a newly manufactured or already installed system. Detection of image artifacts can adapt or learn as new scans are acquired using supervised or semi-supervised learning. Assessment of system imaging performance in the recently manufactured as well as the installed base can be performed reliably and automatically.