Invention Grant
- Patent Title: Image synthesis using adversarial networks such as for radiation therapy
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Application No.: US15952686Application Date: 2018-04-13
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Publication No.: US11100632B2Publication Date: 2021-08-24
- Inventor: Xiao Han
- Applicant: Elekta, Inc.
- Applicant Address: US GA Atlanta
- Assignee: Elekta, Inc.
- Current Assignee: Elekta, Inc.
- Current Assignee Address: US GA Atlanta
- Agency: Schwegman Lundberg & Woessner, P.A.
- Agent Sanjay Agrawal
- Main IPC: G06T7/00
- IPC: G06T7/00

Abstract:
A statistical learning technique that does not rely upon paired imaging information is described herein. The technique may be computer-implemented and may be used in order to train a statistical learning model to perform image synthesis, such as in support of radiation therapy treatment planning. In an example, a trained statistical learning model may include a convolutional neural network established as a generator convolutional network, and the generator may be trained at least in part using a separate convolutional neural network established as a discriminator convolutional network. The generator convolutional network and the discriminator convolutional network may form an adversarial network architecture for use during training. After training, the generator convolutional network may be provided for use in synthesis of images, such as to receive imaging data corresponding to a first imaging modality type, and to synthesize imaging data corresponding to a different, second imaging modality type.
Public/Granted literature
- US20190318474A1 IMAGE SYNTHESIS USING ADVERSARIAL NETWORKS SUCH AS FOR RADIATION THERAPY Public/Granted day:2019-10-17
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