Magnetic resonance multi-channel reconstruction method based on deep learning and data self-consistence
A technology of deep learning and magnetic resonance, applied in magnetic resonance measurement, measurement using nuclear magnetic resonance image system, measurement of magnetic variables, etc., can solve the problems of low acceleration factor, low processing capacity of high-magnification undersampling data, etc., to improve quality , improve the generalization ability and shorten the time
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Example Embodiment
[0043] Example 1
[0044] A magnetic resonance multi-channel reconstruction method based on deep learning and data self-consistent, including the following steps:
[0045] Step 1: Collect multi-channel full-sampled training set K-space data, convert the under-sampled K-space data into curled images as input to the reconstruction network, and use the full-sampled K-space data as the training label data of the reconstruction network based on Fully sampled K-space data to generate convolution kernel;
[0046] Step 2: After inputting the rolled image and fusing the under-sampled K-space data and the convolution kernel, the reconstruction network constructed by repeatedly superimposing the SC layer, CNN network and DC layer, using the training label data as the target, and training the network through back propagation The parameter gets the mapping relationship between the input and output of the reconstructed network;
[0047] Step 3: Input the under-sampled test set data into the recons...
Example Embodiment
[0049] Example 2
[0050] Step 1 includes the following steps:
[0051] Step 1.1: Use the multi-channel receiving coil to collect the K-space data of the full-sample training set, and generate the under-sampled K-space data S through artificial under-sampling u , Its size is N x *N y *N c , Where N x Represents the number of rows of collected data, N y Represents the number of columns of data, N c Represents the number of receiving channels; manual under-sampling does not require manual operation by humans, and can be automated with a variety of algorithms; in the training phase, the under-sampling data is generated by discarding some of the full-sampled data; in the testing phase, the magnetic resonance scanner Under-sampled data can be obtained directly; a full-sampled data can only have one corresponding under-sampled data, so in actual operation, after a full-sampled data is obtained, the under-sampled data is generated artificially on this basis.
[0052] Step 1.2: Undersample K...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap