CT image super-resolution reconstruction method based on generative adversarial network

A super-resolution reconstruction and CT image technology, which is applied in biological neural network models, image data processing, neural learning methods, etc., can solve problems affecting doctors' accurate diagnosis, CT image quality degradation, and reduction of projection data, etc.

Active Publication Date: 2019-11-12
TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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However, when the radiation dose is reduced, the output of projection data will be reduced...

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  • CT image super-resolution reconstruction method based on generative adversarial network
  • CT image super-resolution reconstruction method based on generative adversarial network
  • CT image super-resolution reconstruction method based on generative adversarial network

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[0067] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0068] Based on the CT image super-resolution reconstruction method based on generative confrontation network, a new generator structure is proposed and a new cost function is designed to reconstruct the CT image by 4 times from 128*128 to 512*512.

[0069] 1. Arrange a multi-layer dense residual block (Multiple Dense Residual Blocks, MDRBs) generator network:

[0070] This application proposes a lightweight multi-level dense residual block structure as the main structure of the generator, such as figure 1 As shown, n residual elements are bundled into a residual ...

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Abstract

The invention belongs to the technical field of computed tomography image processing. According to the specific technical scheme, the CT image super-resolution reconstruction method based on the generative adversarial network comprises the following specific steps: 1, establishing a dense connection relationship among different residual blocks based on a multi-stage dense residual block generatornetwork; 2, adding a bottleneck layer to the front end of each dense residual block; 3, optimizing the global network by adopting the Wasserstein distance loss and the VGG feature matching loss; 4, arranging a multi-path generator based on the sequence from thick to thin; 5, generating an image based on conditional expression generative adversarial learning; 6, reconstructing a CT image super-resolution reconstruction framework of the generative adversarial network based on multiple paths of conditions from coarse to fine; 7, reconstructing a loss function. According to the method, network redundancy is reduced, feature multiplexing among different residual blocks is realized, the maximum information transmission of the network is realized, the feature utilization rate is improved, and thereconstructed image quality is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of CT image processing, and in particular relates to a CT image super-resolution reconstruction method based on a generative confrontation network. Background technique [0002] Computed Tomography (Computed Tomography, CT) technology has become an important means of inspection in today's medicine, and is currently widely used in the fields of clinical inspection and medical research. CT imaging technology was proposed as early as 1940. After that, Gabriel Frank established the theoretical thought from CT projection to CT reconstruction, which laid a solid theoretical foundation for the development of CT technology. In 1972, Hounsfield successfully developed the world's first computed tomography scanner, successfully applied CT imaging and reconstruction theory to the medical field, and achieved remarkable results, which also promoted the rapid development of medical CT technology, which has been widely use...

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Application Information

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IPC IPC(8): G06T11/00G06T3/40G06N3/04G06N3/08
CPCG06T11/003G06T3/4053G06N3/084G06N3/045
Inventor 张雄宁爱平冯聪丽上官宏王安红武有成
Owner TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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