Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.