Nuclear magnetic resonance brain image blood vessel reconstruction method based on 3T and 7T

Abstract

The invention provides a nuclear magnetic resonance brain image blood vessel reconstruction method based on 3T and 7T, and the method comprises the following steps: 1, obtaining a 3T image and a 7T image, carrying out the image preprocessing of the 3T image and the 7T image, and obtaining a preprocessed 3T image and a preprocessed 7T image; 2, based on U-net, taking the preprocessed 3T picture asinput of a U-net network to obtain an output picture through the U-net; 3, respectively inputting the output picture of U-net and preprocessed 7T picture into VGG-16 network, obtaining output of a 3Tpicture passing through VGG-16 and output of a 7T picture passing through VGG-16 network; 4, performing loss calculation on output of the U-net,and output of a 3T picture passing through VGG-16 and output of a 7T picture passing through VGG-16 network, obtaining parameters through a stochastic gradient descent method based on a loss function, and updating the U-net network according to the parameters, and inputting the 3T picture into the updated U-net network to obtain a reconstruction result. According to the invention, the blood vessel can be reconstructed on the 3T picture.

Description

technical field [0001] The invention relates to the technical field of magnetic resonance imaging, in particular to a 3T and 7T-based nuclear magnetic resonance brain image blood vessel reconstruction method. Background technique [0002] 7T MRI images can clearly see blood vessels that 3T MRI images do not have, but due to the high cost and scarcity of 7T equipment, most hospitals still use 3T images. Therefore, aiming at this problem, it is very necessary to propose a vascular reconstruction method based on 3T and 7T MRI brain images to overcome this problem. [0003] Most of the existing domestic and foreign researches on image reconstruction focus on super-resolution image reconstruction. In 2014, Dong et al. proposed a CNN model SRCNN for general natural image super-resolution reconstruction. Based on SRCNN, Kim et al. proposed VDSR by referring to the VGG network structure for image classification. Bee Lim et al. proposed an enhanced deep residual network EDSR. [...

AI Technical Summary

Problems solved by technology

[0002] 7T magnetic resonance images can clearly see blood vessels that 3T magnetic resonance images do not have, but due to the high cost and scarcity of 7T equipment, most hospitals still use 3T images

[0007] (2) Unlike under-sampled low-resolution images and high-resolution images, 3T and 7T images are not completely registered, and it is difficult to reconstruct images directly using the differences between images

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