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Undersampled CT image reconstruction method

A CT image, under-sampling technology, applied in the field of digital image processing, can solve the problem that the filtered back-projection algorithm cannot cope with the under-sampled projection data

Active Publication Date: 2015-12-02
SUZHOU INST OF BIOMEDICAL ENG & TECH CHINESE ACADEMY OF SCI
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Problems solved by technology

Aiming at the disadvantage that the filtered back-projection algorithm cannot cope with under-sampled projection data, this case proposes an iterative reconstruction algorithm based on dictionary learning theory combined with weighted penalty factors

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  • Undersampled CT image reconstruction method

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Embodiment Construction

[0058] The present invention will be further described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can implement it with reference to the description.

[0059] This case provides an undersampled CT image reconstruction method of an embodiment, comprising the following steps:

[0060] Step 1) load the raw CT scan data to be processed;

[0061] Step 2) An iterative reconstruction algorithm based on dictionary learning, assuming that the regularization parameter λ is positive infinity, obtain the preliminary reconstructed image under this condition

[0062] Step 3) Calculate the preliminary reconstructed image The relative error δ between the forward projection data and the original CT scan data λ→∞ ;

[0063] Step 4) Construct an adaptive selection model of the regularization parameter λ, which adopts the relative error δ λ→∞ as an independent variable to obtain the value of the regularization parameter λ that matches th...

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Abstract

The invention relates to an undersampled CT image reconstruction method. At first, values of regularization parameters in the object functions of an iteration algorithm are determined by a trained and constructed self-adaptive parameter selection model, and then an iteration reconstruction model based on a dictionary learning theory is built, weight penalty factors are added to the regularization bound items of the object functions to form a weight dictionary learning iteration reconstruction model, every iteration process of the model alternatively updates the sparse representation of the weight penalty factors, the self-adaptive dictionary, and image blocks and reconstructs the images, and the iteration stopping condition is reached at the end. The undersampled CT image reconstruction method can be better adapted to the undersampled condition with insufficient projection data to meet the need of reducing the radiation dosege, and when the data sampling rate is reduced to a certain degree, a CT image can be reconstructed in high quality, and good robustness is provided for the projection noises caused by low dosage.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an image reconstruction method for performing data processing on original projection data obtained by subsampling CT scanning. Background technique [0002] A representative CT image reconstruction algorithm in the prior art is an analytical reconstruction method based on filtered back projection. This algorithm is based on the central slice theorem, that is, the value of the one-dimensional Fourier transform function of the projection data of the two-dimensional image in a certain direction and the two-dimensional Fourier transform function of the image pass along the same direction on the frequency domain plane. The values ​​on the line at the origin are equal. Therefore, the filtered back projection algorithm obtains a series of one-dimensional Fourier transform functions through the projection data in various directions, and these functions are weighted and ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00
Inventor 郑健章程张寅郁朋袁刚吴中毅
Owner SUZHOU INST OF BIOMEDICAL ENG & TECH CHINESE ACADEMY OF SCI
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