Canon Medical Systems Corporation

Yi Hu

John Baumgart

Joseph Manak

Yu-Bing Chang

Haruki Iwai

James Begelman

Rui Hua

A system and method is provided for controlling an acquisition system to obtain, based on a currently acquired set of projection data, a next set of projection data that is to be acquired using a set of learned imaging conditions. The set of learned imaging conditions are, at least in part, based on training data including projection data and dose information. In one embodiment, the initial dose and angle for obtaining the initial set of projection data are chosen at random. In another embodiment, the initial dose and angle for obtaining the initial set of projection data are chosen based on a test scan and a trained neural network for outputting the initial dose and angle using a loss function.

Apparatus, method, and non-transitory computer-readable storage medium for generating a task-aware dynamic sparse-view scanning procedure

A method includes obtaining, at a local imaging system, projection data for an object representing an intensity of radiation detected along a plurality of rays through the object using a first set of imaging parameters; transmitting an image quality dataset related to the obtained projection data to a remote server; generating, via the remote server, localized restoration information based on the received image quality dataset; transferring the localized restoration information from the remote server to the local imaging system; and updating the local imaging system using the localized restoration information.

Method and system for automatic deployment of image restoration parameters

A general workflow for deep learning based image restoration in X-ray and fluoroscopy/fluorography is disclosed. Higher quality images and lower quality images are generated as training data. This training data can further be categorized by anatomical structure. This training data can be used to train a learned model, such as a neural network or deep-learning neural network. Once trained, the learned model can be used for real-time inferencing. The inferencing can be more further improved by employing a variety of techniques, including pruning the learned model, reducing the precision of the learned mode, utilizing multiple image restoration processors, or dividing a full size image into snippets.

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Vernon Hills, IL, United States of America

Yi Hu