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Image enhancement using generative adversarial networks

A generative, image technology, applied in image enhancement, biological neural network model, image analysis, etc.

Pending Publication Date: 2021-01-08
ELEKTA AB
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although researchers have conducted many studies and developed several related methods to reduce artifacts in CBCT images, there is currently no simple and effective method that can suppress all or most common artifacts

Method used

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  • Image enhancement using generative adversarial networks
  • Image enhancement using generative adversarial networks
  • Image enhancement using generative adversarial networks

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Embodiment Construction

[0036] The present disclosure includes various techniques for improving and enhancing CBCT imaging by generating sCT images (synthetic CT images or simulated CT images representing received CBCT images), including to provide improved CBCT images compared to manual (e.g. , being guided, assisted or guided by humans) and conventional methods in a way that has technical advantages. These technical advantages include: reduced computational processing time to generate enhanced CBCT images or sCT images, removal of artifacts in CBCT images and enhanced CBCT images, and concomitant improvements to the processing, memory, and networking used to generate and enhance CBCT images and sCT images Resource improvements. In addition to improvements in data management, visualization, and control systems used to manage data to support these improved CBCT images or sCT images, these improved CBCT images or sCT images can also be applicable to a wide variety of medical treatment and diagnostic s...

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Abstract

Techniques for generating an enhanced cone-beam computed tomography (CBCT) image using a trained model are provided. A CBCT image of a subject is received. a synthetic computed tomography (sCT) imagecorresponding to the CBCT image is generated, using a generative model. The generative model is trained in a generative adversarial network (GAN). The generative model is further trained to process the CBCT image as an input and provide the sCT image as an output. The sCT image is presented for medical analysis of the subject.

Description

[0001] This application for patent claims the benefit of priority to U.S. Application Serial No. 16 / 044,245, filed July 24, 2018, which claims the title The entire contents of each of these two applications are hereby incorporated herein by reference in their entirety. technical field [0002] Embodiments of the present disclosure generally relate to cone beam computed tomography (CBCT) imaging, computed tomography imaging, and artificial intelligence processing techniques. In particular, the present disclosure relates to the generation and use of data models in generative adversarial networks (GANs) suitable for use with CBCT and computed tomography images and system operations. Background technique [0003] X-ray cone-beam computed tomography (CBCT) imaging has been used in radiotherapy for patient setup and adaptive re-planning. In some cases, CBCT imaging has also been used for diagnostic purposes, such as dental imaging and implant planning. Furthermore, X-ray CBCT im...

Claims

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Application Information

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IPC IPC(8): G06T11/00
CPCG06T11/008G06T2207/20081G06T2207/20084G06T2207/10081G06N3/084G06N3/047G06N3/048G06N3/045G06T5/60G06T7/0014G06N3/088G06F17/18
Inventor 徐峤峰韩骁
Owner ELEKTA AB
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