Xiamen University

Congbo Cai

Shuhui Cai

Jian Wu

Zhong Chen

Qizhi Yang

Chenlu Guo

Wenhua Geng

A method for fast high-resolution multi-parametric quantitative magnetic resonance imaging comprises: designing a fast high-resolution multiple overlapping-echo imaging pulse sequence; determining sampling parameters of the pulse sequence; constructing a deep neural network for reconstructing high-resolution multi-parametric quantitative magnetic resonance images; generating training samples of the deep neural network; using the training samples to train the deep neural network to obtain trained deep neural networks; scanning a real imaging object using the pulse sequence under the sampling parameters to obtain k-space data of the real imaging object; pre-processing the k-space data of the real imaging object to obtain image domain data of the real imaging object; and inputting the image domain data of the real imaging object into the trained deep neural networks for the reconstructing to obtain the high-resolution multi-parametric quantitative magnetic resonance images of the real imaging object.

Method and system for high-resolution multi-parametric quantitative magnetic resonance imaging

A method and system for simultaneous quantitative multiparametric MRI are provided in the disclosure. The method includes: designing a magnetic resonance imaging (MRI) sequence which includes a signal excitation part, a shift gradient part, and a data acquisition part; generating training samples for a deep neural network according to the MRI sequence; training the deep neural network by using the training samples, to obtain a trained deep neural network; and reconstructing multiple magnetic-resonance parametric maps by using the trained deep neural network and k-space data acquired by the MRI sequence. The disclosure can implement quantitative multiparametric MRI, and can correct image distortions caused by magnetic field inhomogeneity.

Method and system for simultaneous quantitative multiparametric magnetic resonance imaging (MRI)

Disclosed is a method for reconstruction of a Chemical Exchange Saturation Transfer (CEST) contrast image. The method includes: generating training samples for a deep neural network; training the deep neural network with the training samples to obtain a trained deep neural network; and reconstructing a CEST contrast image by using the trained deep neural network and PROPELLER undersampled CEST images. The method for reconstruction of a CEST contrast image can effectively shorten the experimental time of a CEST contrast imaging and can obtain a smoother and more accurate CEST contrast image. Further disclosed is a system for reconstruction of a CEST contrast image to implement the method for reconstruction.

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Fujian, China

Congbo Cai