A multi-sequence magnetic resonance image registration method based on cyclic generation antagonistic network

Abstract

A multi-sequence magnetic resonance image registration method based on cyclic generation antagonistic network comprises the following steps: 1) training the original magnetic resonance images of sequence 1 and sequence 2 with CycleGAN, and outputting the generated magnetic resonance images of sequence 1 and sequence 2; 2) performing single mode registration on the generated image and the originalimage of the same sequence, and calculating a transformation matrix and a similarity measurement between the two images of the same sequence; 3) comparing similarity measures of two sequences, selecting corresponding strategies, and outputting final transformation matrix; 4) using final transformation matrix to transform floating graph to obtain final result graph. The invention has less dependence on registered samples, higher network trainability, stronger anti-interference ability, and higher registration accuracy.

Description

technical field [0001] The invention relates to a multi-sequence magnetic resonance image registration method. Background technique [0002] Different weighted images are used in the acquisition of magnetic resonance images, resulting in different image representations of different sequences of magnetic resonance images. If the features are directly searched for the images, it is often impossible to match consistent features. Therefore, finding consistent features in the process of multi-sequence MRI image registration becomes a key issue for this type of registration, that is, converting MRI images of different sequences into images of the same sequence, and then using single-modality image registration method for alignment. In the traditional machine learning image generation technology, a large amount of registered data is often required for training, such as the image generation technology based on structured random forest. The introduction of the method of generative ...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the problem of insufficient anti-interference ability of existing generative adversarial networks in the conversion process of multi-sequence magnetic resonance images and the difficulty of network training, the present invention provides an easy-to-train network and strong anti-interference ability combined with cyclic generative adversarial A network-based transformation method for multi-sequence magnetic resonance image registration

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