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U-net based generation adversarial network DSA imaging method and device

An imaging method and network technology, applied in the field of XR imaging, can solve problems such as insufficient removal of artifacts, achieve the effects of reducing the number of DSA scans, reducing radiation dose damage, and excellent processing effects

Pending Publication Date: 2019-08-23
SOUTHEAST UNIV
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Problems solved by technology

[0006] The present invention is aimed at the deficiencies of the prior art technology for removing motion artifacts in DSA imaging, and provides a DSA imaging method based on generative adversarial networks. The ability of vascular subtraction, and overcome the problem that the existing traditional methods cannot fully remove the artifacts with large motion range

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  • U-net based generation adversarial network DSA imaging method and device
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Embodiment 1

[0037] Embodiment 1: as figure 1 As shown, a full-network vascular subtraction DSA imaging method based on the U-net generative confrontation network disclosed in the embodiment of the present invention utilizes the powerful feature representation ability of the U-net convolutional neural network and combines the multi-scale capabilities of the U-net With the advantages of confrontation and generation, a full network is established to handle the digital angiography subtraction DSA imaging process.

[0038] The details are as follows, at first the contrast frame image and the corresponding silhouette frame image in the training set are de-averaged and normalized. Similarly, the input and output data components are according to a certain size (the image size is 512 * 512 * 1), the implementation of the present invention The U-net convolutional neural network consists of two parts: a contracting path to obtain context information and a symmetrical expanding path for precise posit...

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Abstract

The invention discloses a U-net based generation adversarial network based digital substration angiography DSA imaging method and device. The method comprises the following steps: firstly, obtaining aplurality of groups of radiography frames and original data corresponding to the subtraction frames; secondly, establishing a U-shaped structure convolutional network, coding and extracting featuresof different scales, and decoding and recovering corresponding features by using a skill connected object, wherein the network inputs a radiography frame and outputs a corresponding subtraction frameto reduce the dependence of DSA generation on a background frame so as to remove motion artifacts generated by motion of a patient; then, by means of generative adversarial training, alternately training the generator and the discriminator, and enhancing the quality of generated digital blood vessel subtraction. According to the method, the motion artifacts in the digital substration angiography (DSA) can be effectively removed, the data quality can meet the requirements of clinical analysis, diagnosis and the like, the DSA imaging quality is improved, and the influence caused by motion of a patient is reduced.

Description

technical field [0001] The invention relates to a method for removing DSA imaging artifacts, in particular to a method and device for removing DSA imaging artifacts based on a generative confrontation network, and belongs to the technical field of XR imaging. Background technique [0002] Digital Subtraction Angiography (Digital Subtraction Angiography) is referred to as DSA. As a radiotherapy method, in the actual imaging process, it is necessary to inject contrast agent intravenously and collect images through X-rays. The image (mask) without contrast agent is subtracted, the tissue image that we are not interested in is eliminated, and the image of the target area containing only the contrast agent is retained, and the image of the prominent vascular structure is obtained. The cut part here is Noise, after the noise is removed, the target area is clearer. This imaging mode can accurately diagnose vascular morphological lesions, improve the rate of disease discovery and d...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06N3/04G06N3/08G06T7/33
CPCG06N3/08G06T7/33G06T2207/30101G06N3/044G06N3/045G06T5/80
Inventor 陈阳宋雨朱洪
Owner SOUTHEAST UNIV
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