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US 11,380,025 B2Granted
Scatter correction method and apparatus for dental cone-beam CT
AssigneeRay Co., Ltd.
InventorsSang Chul Lee, Sung Ho Chang
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AssigneeRay Co., Ltd.
InventorsSang Chul Lee, Sung Ho Chang
Estimating the term…
The present invention relates to scatter correction method and apparatus for dental cone-beam CT. An object of the present invention is improving quality of reconstructed images by processing the scatter correction by learning which uses Monte Carlo simulation and artificial neural network. In order to achieve this object, the scatter correction method is characterized in that the method comprises steps of: rotating X-ray source of cone-beam CT in a predetermined angle while obtaining CT images for respective angles with flat-panel detector so as to reconstruct 3-dimensional CT image; generating a 2D profile of projection image by Monte Carlo simulation for respective angles by use of the reconstructed 3-dimensional CT image; decomposing the 2D profile of projection image so as to separate primary x-ray image and scatter image, wherein the primary x-ray image is unscattered in reaching the detector and wherein the scatter image is generated only by the scatter; building and doing learning of artificial neural network, wherein the objective function of the artificial neural network is primary image and scatter image which have been generated in simulation and wherein the input of the artificial neural network is the projection image which have been obtained in reality; and storing the learning information for the artificial neural network and then applying the learning information to scatter correction.
A scatter correction method for dental cone-beam CT, the method comprising:
Scatter correction apparatus for dental cone-beam CT, the apparatus having computer program which comprising:
Scatter correction apparatus for dental cone-beam CT, the apparatus having computer program including an artificial neural network of having input of projection images for respective angles based on the CT image, of having output of X-ray image with scatter removed wherein the X-ray image is generated based on learning data, and of determining scatter profile from the projection image so as to output image which be removed from scatter, wherein the artificial neural network utilizes, as objective function of learning, the scatter image which is obtained by Monte Carlo simulation on X-ray projection image for respective angles with respect to a pre-acquired CT image of subject, and wherein surface fitting is performed on the scatter image by least-square or optimization so as to remove statistical noise out of the scatter image for utilized as the objective function of learning.
Dependencies are inferred from the claim text.