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NMR relaxation time inversion method based on non-training deep neural network

A technology of deep neural network and relaxation time, applied in the field of NMR relaxation time inversion based on untrained deep neural network, which can solve the problems of poor performance and generalization error

Active Publication Date: 2021-12-03
INNOVATION ACAD FOR PRECISION MEASUREMENT SCI & TECH CAS
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

But a fundamental problem with the performance of trained deep neural networks in predicting outcomes on untrained data is generalization error
When the test data deviates from the training data, the results predicted by this type of method will be poor

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  • NMR relaxation time inversion method based on non-training deep neural network
  • NMR relaxation time inversion method based on non-training deep neural network
  • NMR relaxation time inversion method based on non-training deep neural network

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Embodiment Construction

[0028] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the examples. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0029] The relaxation time includes a transverse relaxation time and a longitudinal relaxation time. In this embodiment, the transverse relaxation time is taken as an example for illustration. The difference between the longitudinal relaxation time and the transverse relaxation time is only in the formula of the relaxation signal. The transverse relaxation signal is a decay signal (decreases with time), while the longitudinal relaxation signal is a recovery signal (increases with time). longitudinal relaxation time T 1 spectra and transverse relaxation times T 2 The inversion a...

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Abstract

The invention discloses an NMR relaxation time inversion method based on a non-training deep neural network. The method comprises the following steps: a non-training deep neural network model is established, and a loss function of the non-training deep neural network model is established; an NMR relaxation signal is input; and the non-training deep neural network model updates the neural network weight according to the input NMR relaxation signal and minimizes the loss function, thereby obtaining the mapping relation between the optimal NMR relaxation signal and the NMR relaxation time spectrum, and outputting the optimal NMR relaxation time spectrum. According to the method, priori information and regularization parameter self-learning are not needed; pre-training is not needed, and a large number of data sets are not depended; and the noise in the to-be-measured data has high impedance.

Description

technical field [0001] The invention belongs to the technical field of nuclear magnetic resonance, and in particular relates to an NMR relaxation time inversion method based on an untrained deep neural network. Background technique [0002] In the field of nuclear magnetic resonance (NMR) research, the NMR relaxation time of the sample being studied is closely related to the structure and dynamic process of the material molecule and the environment in which it is located, and is a characteristic parameter that characterizes the relationship between the properties of the material and the environment in which it is located. There are two types of NMR relaxation times most commonly used in research: the longitudinal (spin-lattice) relaxation time T 1 and the transverse (spin-spin) relaxation time T 2 . For the NMR sample of a simple system (such as pure water), the relaxation process is a monoexponential time-varying function, and the relaxation time of the sample (T 1 and T...

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Application Information

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IPC IPC(8): G06N3/04G06N3/063G06N3/08
CPCG06N3/049G06N3/063G06N3/08G06N3/084
Inventor 陈黎申胜陈俊飞陈方刘朝阳
Owner INNOVATION ACAD FOR PRECISION MEASUREMENT SCI & TECH CAS