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Detail enhancement-based T1 to STIR image conversion method

An image conversion and image technology, applied in the field of medical image conversion, can solve the problems of easy loss and blurring of images

Pending Publication Date: 2020-12-15
ZHEJIANG UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a method for image conversion from T1 to STIR based on detail enhancement, so as to solve the technical problem in the prior art that the image details after image conversion are easily lost and blurred

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  • Detail enhancement-based T1 to STIR image conversion method
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  • Detail enhancement-based T1 to STIR image conversion method

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Embodiment Construction

[0102] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0103] see figure 1 , an embodiment of the present invention provides a method for converting T1 to STIR images based on detail enhancement, the method comprising the following steps:

[0104] S100, preprocessing the acquired neurofibroma data;

[0105] S200, constructing a generative adversarial neural network based on T1 image conversion to STIR image;

[0106] S300, setting hyperparameters of the adversarial neural network model, and importing the preproc...

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Abstract

The invention discloses a detail enhancement-based T1 to STIR image conversion method, which comprises the following steps: preprocessing acquired neurofibroma data, constructing a generative adversarial neural network based on T1 image to STIR image conversion, setting hyper-parameters of an adversarial neural network model, and establishing an adversarial neural network model; importing the preprocessed data into an adversarial neural network in a minibatch form for training until convergence of the adversarial neural network reaches a predetermined condition, inputting the preprocessed testdata into the trained adversarial neural network for conversion to obtain a residual template, fusing the residual template and a T1 image to obtain a synthesized STIR image, and evaluating the synthesized STIR image. According to the conversion method, the details and features of the image can be restored to a greater extent by training the residual template, so that the conversion effect of theimage details is improved.

Description

technical field [0001] The invention relates to the technical field of medical image conversion, in particular to a method for converting T1 to STIR images based on detail enhancement. Background technique [0002] Magnetic Resonance Imaging (MRI, Magnetic Resonance Imaging) uses the content of hydrogen protons to image. Water contains the most hydrogen protons in the human body, so it can be roughly understood as water imaging. Previous studies have shown that MRI can be used to evaluate neoplastic lesions throughout the body, including bone and soft tissue tumors and neurofibromas. And MRI itself has different imaging methods, such as T1 (T1-weighted imaging, T1-weighted imaging), T2 (T2-weighted image, T2-weighted imaging), STIR (Short TI Inversion-Recovery, short-term inversion recovery), etc. . [0003] In fact, tissues can be presented in any imaging method, but different imaging methods have different emphases. Among them, T1 images are superior to STIR images in p...

Claims

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Application Information

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IPC IPC(8): G06T5/50G06N3/04G06N3/08G06T7/33
CPCG06T5/50G06N3/08G06T7/33G06T2207/10088G06T2207/20228G06T2207/20221G06N3/045
Inventor 严森祥严凡严丹方陈为
Owner ZHEJIANG UNIV
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