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CT image block reconstruction method and system based on deep learning

A CT image and deep learning technology, applied in the field of X-ray CT imaging, can solve the problems of many training parameters and large projection matrix scale, and achieve the effects of increasing training samples, strong reconstruction ability, and shortening training time

Active Publication Date: 2022-03-15
CAPITAL NORMAL UNIVERSITY
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Problems solved by technology

[0005] Aiming at the deficiencies of the existing technology, in order to solve the problem that the scale of the system projection matrix is ​​too large, too many training parameters, etc., the present invention proposes a CT image block reconstruction method and its system based on deep learning

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Embodiment Construction

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the figures in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0062] Those skilled in the art should understand that this embodiment is only a preferred implementation manner, and should not be construed as limiting the protection scope of the present invention.

[0063] First, based on the characteristics of X-rays, in a specific embodiment, the intensity of X-rays is attenuated after passing through the measured object, and the calculation formula of the attenuated polychromatic projection is as follows:

[0064]

[0...

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Abstract

The present invention provides a CT image block reconstruction method and system based on deep learning. The system includes: a CT projection data filtering module, which performs local range filtering and full-area filtering on the input CT projection data; an image block reconstruction module, which calculates The projection data position corresponding to the current image block to be reconstructed, and extracting the filtered projection data at the projection data position; and connecting the extracted filtered projection data with each pixel of the image block, and reconstructing the image blocks, and regularize the reconstructed image blocks; then filter the regularized image blocks; the image synthesis module synthesizes all the reconstructed image blocks to obtain the synthesized image; image reconstruction and output module , filtering the synthesized image, obtaining a reconstructed image, and outputting it. The invention improves the image reconstruction ability, saves the projection iteration step, and has fast reconstruction speed, and is suitable for situations such as inconsistent responses of detection units.

Description

technical field [0001] The present invention relates to the technical field of X-ray CT imaging, in particular to a CT image reconstruction method and system based on deep learning. Background technique [0002] X-ray CT imaging technology is a technology that uses the measured projection data to reconstruct the image of the measured object. Mathematically, this is a problem of solving a large-scale nonlinear equation system, and the solving process is relatively complicated. The X-ray energy emitted by commonly used X-ray sources has a wide energy spectrum, and the scattering effect is obvious in the commonly used energy range. However, because the energy spectrum is difficult to accurately calibrate and the scattering effect is also difficult to accurately estimate, the traditional CT image reconstruction algorithm, whether it is an analytical algorithm or an iterative algorithm, does not consider the influence of energy spectrum and scattering, etc., and transforms the CT...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06T5/50G06N3/04
CPCG06T5/006G06T5/50G06T2207/10081G06T2207/20221G06T2207/20081G06N3/045
Inventor 赵星马根炜
Owner CAPITAL NORMAL UNIVERSITY
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