Unlock instant, AI-driven research and patent intelligence for your innovation.

CT image geometric artifact evaluation method based on residual network

A technique for geometric artifacts and CT images, applied in image data processing, neural learning methods, 2D image generation, etc., can solve problems such as image blur, geometric artifacts in CT reconstruction images, and affect the quality of CT reconstruction images, etc., to achieve High accuracy, improve the effect of graded evaluation effect

Pending Publication Date: 2021-12-31
PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] The deviation between the actual system geometric parameters and the ideal geometric parameters leads to geometric artifacts in CT reconstruction images
Geometric artifacts seriously affect the quality of CT reconstruction images, resulting in blurred images and ghosting at the edges, which is one of the key factors hindering the development of CT to intelligent and high-precision

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • CT image geometric artifact evaluation method based on residual network
  • CT image geometric artifact evaluation method based on residual network
  • CT image geometric artifact evaluation method based on residual network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the present invention Examples, not all examples. 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.

[0029] Such as figure 1 As shown, the embodiment of the present invention provides a residual network-based CT image geometric artifact evaluation method, including the following steps:

[0030] S101: Based on the CT image characteristics, make a matching training sample data set;

[0031] Specifically, the deviation of system geometric parameters reflects the severity of geome...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a CT image geometric artifact evaluation method based on a residual network. The method comprises the steps of 1, making a matched training sample data set based on CT image characteristics; 2, taking a residual network Resnet50 as a basic network, and obtaining a geometric artifact evaluation network by reducing the step length of a convolution residual block and adding an attention module design; 3, training the geometric artifact evaluation network by using the training sample data set; and 4, inputting the CT image to be evaluated into the trained geometric artifact evaluation network to obtain the geometric artifact level of the CT image to be evaluated. The geometric artifact degree of the CT image can be evaluated with high accuracy.

Description

technical field [0001] The invention relates to the technical field of CT image processing, in particular to a method for evaluating geometric artifacts of CT images based on a residual network. Background technique [0002] The deviation between the actual system geometric parameters and the ideal geometric parameters leads to geometric artifacts in CT reconstructed images. Geometric artifacts seriously affect the quality of CT reconstructed images, resulting in blurred images and ghosting at the edges, which is one of the key factors hindering the development of CT to intelligent and high-precision. Geometric artifact correction is the prerequisite for CT system to obtain high-quality 3D reconstruction images. The performance of geometric artifact correction methods determines the accuracy of CT images. Evaluating the severity of image geometric artifacts can describe the degree of system geometric parameter mismatch and be used for CT system quality control. On the othe...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06T11/00G06N3/04G06N3/08
CPCG06T11/008G06N3/08G06N3/045
Inventor 韩玉朱明婉李磊闫镔席晓琦朱林林谭思宇孙艳敏亢冠宇杨双站
Owner PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU