Magnetic resonance intervention image reconstruction method based on recurrent neural network
A technology of cyclic neural network and image reconstruction, which is applied in the field of image processing, can solve the problems of slow image reconstruction speed of compressed sensing method, inability to meet the requirements of fast reconstruction of interventional images, and unsatisfactory acceleration factor, etc.
Active Publication Date: 2021-07-30
SHANGHAI JIAO TONG UNIV
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[0003] However, the shortcomings of the above-mentioned technologies include that the acceleration factor of conventional parallel imaging and short TR acquisition methods cannot meet the requirements of real-time imaging, the image reconstruction resolution of partial k-space acquisition and key-hole methods is low, the image quality is not good, and non-Cartesian coordinates In the case of high downsampling, the reconstructed image quality of the K-space acquisition system is poor, the image reconstruction speed of the compressed sensing method is slow, and cannot meet the real-time requirements, and most of the machine learning-based algorithms are used in the reconstruction of magnetic resonance structural images Meet the requirements for fast reconstruction of interventional images
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[0014] Due to the undersampling acquisition signal y=F in MRI image reconstruction u x+e, where: x is a fully sampled image, F u is the downsampled Fourier encoding matrix, e is the noise at the time of acquisition, so recovering x from y is an ill-posed problem, and reconstruction by zero-padding y will result in an aliased undersampled image x unI .
[0015] According to prior knowledge, between different under-sampled images, because the background information is related to each other, there is temporal consistency, and the neural network based on convolution-long short-term memory module (Conv-LSTM) Networks can take advantage of temporal consistency. Therefore, the cyclic neural network adopted in this embodiment reconstructs the undersampled magnetic resonance images of five consecutive frames, and the input of the trained cyclic neural network is the fully sampled preoperative reference image x ref and five consecutive frames of undersampled images t=1~5, the outpu...
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A magnetic resonance interventional image reconstruction method based on a recurrent neural network adopts the recurrent neural network to reconstruct five continuous frames of undersampled magnetic resonance images, the input of the trained recurrent neural network is a full-sampled preoperative reference image and the five continuous frames of undersampled images, and the output of the trained recurrent neural network is a reconstructed image. According to the invention, rapid acquisition and real-time reconstruction of the image can be realized, and the image meeting the navigation demand quality is obtained.
Description
technical field [0001] The present invention relates to a technology in the field of image processing, in particular to a method for reconstructing images of magnetic resonance intervention based on a cyclic neural network. Background technique [0002] Magnetic resonance images have good soft tissue contrast and various imaging methods, which provide an important way for current image-guided surgery and interventional operations. However, the acquisition time of magnetic resonance imaging is long, and it is difficult to carry out real-time image acquisition and image reconstruction in the process of intraoperative operation. Existing MRI fast acquisition and reconstruction methods include: parallel imaging method and short TR acquisition method, partial k-space acquisition, such as key-hole method, non-Cartesian coordinate system k-space acquisition, such as radial acquisition and helical acquisition method, Compressed sensing methods and machine learning methods. [0003...
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Login to View More IPC IPC(8): G06T11/00G06T5/10G06N3/04G06N3/08
CPCG06T11/005G06T11/006G06T5/10G06N3/08G06T2207/10088G06T2207/20056G06T2207/20081G06T2207/20084G06N3/045
Inventor冯原赵睿洋杜一平梁志培
OwnerSHANGHAI JIAO TONG UNIV