A magnetic resonance spectrum reconstruction method based on deep learning

Abstract

The invention discloses a magnetic resonance spectrum reconstruction method based on deep learning, and relates to a magnetic resonance spectrum reconstruction method. The method includes: Generatinga time domain signal of the magnetic resonance spectrum by utilizing an exponential function; Establishing a training set of the under-sampling time domain signals and the full-sampling spectrum; Designing a convolutional neural network in the data verification convolutional neural network structure; Designing a bottleneck layer in the data verification convolutional neural network structure; Designing a data verification layer in the data verification convolutional neural network structure; Designing a feedback function in a data verification convolutional neural network structure; Establishing a data verification convolutional neural network structure as a spectrum reconstruction model; Training network optimization parameters; Reconstructing an under-sampling magnetic resonance time domain signal of the target; When undersampling operation is carried out in the time-frequency domain, utilizing the strong fitting capability of the convolutional neural network and the data verification capability of the data verification layer to complete rapid and high-quality reconstruction of the undersampling magnetic resonance spectrum signal.

Description

technical field [0001] The present invention relates to a magnetic resonance spectrum reconstruction method, in particular to a deep learning-based magnetic resonance spectrum reconstruction method. Background technique [0002] Magnetic Resonance Spectroscopy (MRS) is a technique for determining molecular structure, which has important applications in the fields of medicine, chemistry and biology. In magnetic resonance spectroscopy, how to reduce the sampling time while ensuring the quality of spectral signals is the key to magnetic resonance spectroscopy. [0003] Traditional MRI reconstruction methods mainly use the mathematical properties of MRI time or frequency signals to reconstruct the spectrum. Qu Xiaobo et al. (Qu X, Mayzel M, Cai J, Chen Z, Orekhov V. Accelerated NMRspectroscopy with low-Rank reconstruction [J]. Angewandte Chemie International Edition, 2015, 54(3): 852-854.) proposed a The reconstruction method of magnetic resonance spectrum based on the low-ran...

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Problems solved by technology

However, this kind of low-rank Hankel matrix reconstruction method has a high time consumption of singular value decomposition in iterative calculation, thus resulting in a long spectral reconstruction time

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