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Low-dose full-body pet image enhancement method based on self-inverse convolution generative adversarial network

An image enhancement, low-dose technology, applied in image enhancement, biological neural network model, image analysis, etc., can solve problems such as image contrast reduction, image noise increase, affecting doctor's diagnosis, etc., to maintain image contrast and improve robustness , the effect of preserving image details and contrast

Active Publication Date: 2022-06-24
浙江明峰智能医疗科技有限公司
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Problems solved by technology

However, reducing the injection dose will lead to increased image noise, decreased contrast, and may eventually affect the doctor's diagnosis
Traditional image post-processing methods including BM3D, NLM, etc. usually lead to problems such as image over-smoothing and image contrast degradation

Method used

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  • Low-dose full-body pet image enhancement method based on self-inverse convolution generative adversarial network
  • Low-dose full-body pet image enhancement method based on self-inverse convolution generative adversarial network
  • Low-dose full-body pet image enhancement method based on self-inverse convolution generative adversarial network

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Embodiment Construction

[0043] refer to Figure 1 to Figure 9 The specific implementation of the low-dose whole-body PET image enhancement method based on the self-deconvolution generative adversarial network of the present invention will be further described.

[0044] The low-dose whole-body PET image enhancement method based on self-deconvolution generative adversarial network uses the collected low-dose PET images and full-dose PET images to train the model, tests the model with the low-dose PET images and the training results, and saves the test results to obtain Low-dose PET image enhancement results.

[0045] like Figure 5 As shown, the above training process is:

[0046] (1) Collect low-dose and full-dose PET images;

[0047] (2) Divide the low-dose and full-dose PET image datasets into training, validation, and test sets;

[0048] (3) Normalize the low-dose and full-dose PET images between 0 and 1 in the training and validation sets;

[0049] (4) In the training set and the validation s...

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Abstract

The invention discloses a low-dose whole-body PET image enhancement method based on self-inverse convolution to generate an adversarial network, which uses collected low-dose PET images and full-dose PET images to train the model, and uses the low-dose PET images and training results to test the model. Save the test results to obtain low-dose PET image enhancement results. The present invention realizes mutual conversion between low-dose and full-dose PET images by using a self-reversing neural network. Since the trained network is self-reversing, it can effectively reduce the noise in the image and maintain the image contrast, and effectively improve the accuracy of the model. robustness. The network model of the present invention has simple structure and high calculation efficiency, can effectively reduce noise in images, effectively retain image details and contrast, and has a quantitative error of less than 10%.

Description

technical field [0001] The invention relates to the technical field of medical imaging equipment, and more particularly to a low-dose whole-body PET image enhancement method based on a self-inverse convolutional generative confrontation network. Background technique [0002] Positron Emission Tomography (Positron Emission Tomography) is a relatively advanced clinical examination imaging technology in the field of nuclear medicine. By injecting a radioactive tracer drug into the human body, a pair of gamma rays generated by the decay of the radionuclide in the drug can obtain the distribution map of the radioactive drug in the human body. Generally, radioactive tracers are selected from the substances necessary for the metabolism of biological life, such as: glucose, protein, nucleic acid, fatty acid, and short-lived radionuclides (such as 18F, 11C, etc.) are marked. The aggregation in metabolism reflects the situation of metabolic activities of life, so as to achieve the pu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G16H30/20G06N3/04G06N3/08
CPCG06N3/08G16H30/20G06T2207/10104G06T2207/20081G06N3/045G06T5/90
Inventor 周龙叶宏伟
Owner 浙江明峰智能医疗科技有限公司
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