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DCE-MRI image generation method of W-type network structure based on space and time information

A technology of DCE-MRI and network structure, applied in the field of DCE-MRI image generation with W-type network structure

Pending Publication Date: 2021-06-29
SHENYANG AEROSPACE UNIVERSITY
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] No researchers have yet applied deep learning methods to the generation of DCE-MRI images in different phases, but it has been widely used to generate unoccluded, higher-quality medical images

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  • DCE-MRI image generation method of W-type network structure based on space and time information
  • DCE-MRI image generation method of W-type network structure based on space and time information
  • DCE-MRI image generation method of W-type network structure based on space and time information

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Embodiment Construction

[0025] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. Where the following description refers to the drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the illustrative examples below are not intended to represent all implementations consistent with the present invention. Rather, they are merely examples of systems consistent with some aspects of the invention as recited in the appended claims.

[0026] In the prior art, commonly used recurrent neural networks, such as long short-term memory networks, add time series information to the network, but they only use various simple linear "gates" to consider the influence of the input of past phases on the current state. When generating DCE-MRI images with complex content and uneven grayscale, the results may not be ideal. Moreover, the influence of the differe...

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Abstract

The invention discloses a DCE-MRI image generation method of a W-type network structure based on space and time information. The DCE-MRI image generation method comprises the following steps: acquiring a training data set; and training a W-type neural network structure model according to the training data set, collecting three-dimensional MRI image data I0 of a T0 time phase before contrast agent injection, inputting the image data I0 into the trained W-type neural network structure model based on space and time information, and generating four-dimensional DCE-MRI time-space sequence images Ii and Ii + 1 of Ti and Ti + 1 after contrast agent injection. According to the method, a W-type image generation network based on space and time information is provided according to the mutual relation between DCE-MRI images of adjacent time phases, the network extracts low-dimensional and high-dimensional features of the images of the adjacent time phases at the same time, and four-dimensional DCE-MRI time-space sequence images are generated through the features and related information of the features.

Description

technical field [0001] The present invention relates to the technical field of medical image processing, in particular to a DCE-MRI image generation method based on a W-shaped network structure of spatial and temporal information. Background technique [0002] Imaging examination can not only detect the existence of lesions and locate the location of lesions through images, but also observe physical characteristics such as the size, shape and density of lesions. Among them, unenhanced magnetic resonance imaging (MRI) can detect the tumor and its invasion to the surrounding tissue according to the location, shape and relationship with the surrounding tissue. However, since the metabolic changes within the tumor tissue may be much earlier than the morphological changes, the diagnosis of tumors by plain MRI may be different from the actual situation. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) after injection of contrast agent obtains continuous dynamic enha...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/00G06T7/62G06N3/08G06N3/063
CPCG06T11/003G06T7/62G06N3/084G06T2207/10096G06N3/065
Inventor 郭薇张国栋宫照煊周翰逊刘智何聪柳昱
Owner SHENYANG AEROSPACE UNIVERSITY