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Full-network low-dose CT imaging method and apparatus based on convolution residual network

A CT imaging, low-dose technology, applied in 2D image generation, image enhancement, image analysis, etc., can solve the problems of inability to separate star-streaked artifacts, noise, and high noise pollution, and meet the requirements of clinical analysis and diagnosis requirements, excellent treatment effects, good clinical analysis and diagnosis requirements

Active Publication Date: 2018-12-28
SOUTHEAST UNIV
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Problems solved by technology

[0006] Purpose of the invention: In view of the deficiencies in the prior art, the purpose of the present invention is to provide a low-dose CT imaging method and device based on convolutional residual neural network, so as to improve the current single post-processing algorithm to solve low-dose images with high noise pollution. ability, and overcome the problem that existing low-dose CT image processing methods cannot separate star-streaked artifacts and noise well

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[0037] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0038] Such as figure 1 As shown, a full-network low-dose CT imaging method based on convolutional residual neural network disclosed in the embodiment of the present invention utilizes the powerful feature representation capability of convolutional residual neural network, and combines projection-based spatial processing and image-based Based on the respective advantages of the two methods of spatial processing, a low-dose CT imaging process for full network processing was established. Specifical...

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Abstract

The invention discloses a full-network low-dose CT imaging method and a device based on a convolution residual network. Firstly, the method obtains a plurality of sets of corresponding CT original projection data under low-dose and normal-dose. Secondly, a convolution residual network (CNN1) is established in the projection space, which inputs low dose CT projection data and outputs processed datato reduce the noise and artifacts in low dose CT projection data and improve the signal-to-noise ratio. Then, the projection data is reconstructed into the image space by FBP based on Ramp filter kernel, and the image space is reprocessed based on convolution residual network (CNN2) to reduce the artifacts and noise in the low dose data. The invention can effectively reduce artifacts and noises in low-dose CT data, the data quality can meet the requirements of clinical analysis, diagnosis and the like, and improves the image quality of low-dose CT imaging.

Description

technical field [0001] The invention relates to a low-dose CT imaging method and device, in particular to a low-dose CT imaging method and device based on a convolutional residual neural network, belonging to the technical field of computerized tomography. Background technique [0002] X-ray computer tomography (X-ray Computer Tomography, CT) technology is an imaging technology that obtains accurate and non-destructive cross-sectional attenuation information of objects through ray projection measurement of objects. It is one of the conventional and effective clinical medical diagnostic tools at present. It has become an indispensable inspection and diagnosis method in the field of medical imaging to provide rich three-dimensional human organ tissue information for clinicians' diagnosis and prevention. However, with the popularity of CT tomography in clinical diagnosis, especially in routine examinations, the radiation dose in CT scanning has attracted more and more attention...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00G06T5/00
CPCG06T11/008G06T2207/10081G06T2207/20081G06T2207/20084G06T5/70
Inventor 陈阳蔡宁尹相瑞赵倩隆刘进罗立民
Owner SOUTHEAST UNIV
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