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Low-dose CT reconstruction method based on interpolation convolutional neural network

A convolutional neural network and low-dose technology, which is applied in the field of low-dose CT reconstruction based on interpolation convolutional neural network, can solve the problems of missing sinusoidal information and artifacts in CT images, and achieves faster training speed and better reconstruction effect. , the effect of increasing stability

Active Publication Date: 2021-03-12
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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AI Technical Summary

Problems solved by technology

Each column in the sinusoidal image corresponds to the projection value of an angle. After the sampling angle is reduced, the information of the sinusoidal image is lost, and artifacts will appear in the reconstructed CT image.

Method used

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  • Low-dose CT reconstruction method based on interpolation convolutional neural network
  • Low-dose CT reconstruction method based on interpolation convolutional neural network
  • Low-dose CT reconstruction method based on interpolation convolutional neural network

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Embodiment

[0037] figure 1 It is a flowchart of a low-dose CT reconstruction method based on an interpolation convolutional neural network in the present invention.

[0038] In this example, if figure 1 Shown, a kind of low-dose CT reconstruction method based on interpolation convolutional neural network of the present invention comprises the following steps:

[0039] S1. Construct a training data set, specifically as figure 2 shown, including the following steps:

[0040] S1.1. Acquire standard-dose CT images of different patients, and perform Radon transformation on each image to obtain a standard-dose projection sinogram;

[0041] S1.2. Quantize the projection sinogram to (0, 1), thereby obtaining a 512×512 standard dose projection sinogram;

[0042] S1.3. Extract the odd-numbered columns to obtain a simulated low-dose projection sinogram with a size of 512×256, and the remaining even-numbered columns are used as the desired interpolation diagram with a size of 512×256;

[0043]...

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Abstract

The invention discloses a low-dose CT reconstruction method based on an interpolation convolutional neural network, and the method comprises the steps: predicting the missing projection data on a CT projection sinogram through the interpolation convolutional neural network through the application of the interpolation convolutional neural network in super-resolution reconstruction, and adding the known projection data to obtain a complete CT projection sinogram; and finally, obtaining a reconstructed CT image through filtering back projection, so that noise on the low-dose CT image can be effectively removed, and a better reconstruction effect is obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, and more specifically relates to a low-dose CT reconstruction method based on an interpolation convolutional neural network. Background technique [0002] At present, people pay more and more attention to the potential harm caused by CT radiation dose to human body. The radiation dose can be reduced by reducing the tube current intensity (low-dose CT) and reducing the number of sampling (sparse-angle CT). But this destroys the completeness of the projection data, and the image quality directly reconstructed by the traditional reconstruction algorithm will be seriously degraded. Therefore, how to reduce the radiation dose while ensuring the quality of reconstructed images has become a hot spot in CT research in recent years. [0003] Aiming at the problem of noise in low-dose projection data and the problem of streak artifacts and noise in reconstructed images, from the perspective of s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00G06T7/12G06T5/00G06K9/62G06N3/04G06N3/08
CPCG06T11/008G06T7/12G06N3/08G06T2207/10081G06T2207/20081G06T2207/20084G06N3/048G06N3/045G06F18/214G06T5/70
Inventor 杨波牛培昕郑文锋刘珊
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA