Invention Grant
- Patent Title: Image quality improvement in cone beam computed tomography images using deep convolutional neural networks
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Application No.: US16739951Application Date: 2020-01-10
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Publication No.: US11080901B2Publication Date: 2021-08-03
- Inventor: Jiaofeng Xu , 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: G06K9/00
- IPC: G06K9/00 ; G06T11/00 ; A61B6/03 ; A61B6/00 ; G06K9/66 ; G06N3/08 ; G06T5/00

Abstract:
Systems and methods include training a deep convolutional neural network (DCNN) to reduce one or more artifacts using a projection space or an image space approach. In a projection space approach, a method can include collecting at least one artifact contaminated cone beam computed tomography (CBCT) projection space image, and at least one corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated and artifact reduced CBCT projection space images to train a DCNN to reduce artifacts in a projection space image. In an image space approach, a method can include collecting a plurality of CBCT patient anatomical images and corresponding registered computed tomography anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced computed tomography anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.
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