Multi-b-value diffusion magnetic resonance imaging optimization method based on signal-to-noise ratio weighting

A technology of magnetic resonance imaging and optimization method, which is applied in the direction of using nuclear magnetic resonance imaging system for measurement, magnetic resonance measurement, and measurement of magnetic variables. It can solve problems that affect technology application, unstable fitting, and meaningless solutions, and achieve solutions The effect of stabilizing, reducing the influence, reducing the influence

Active Publication Date: 2016-12-07
TIANJIN UNIV
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Problems solved by technology

Therefore, the sensitivity of the model to the signal-to-noise ratio of the data is also becoming more and more sensitive. Therefore, in practical applications, due to the singular value of the model fitting error, the fitting instability, and the meaningless solution are gradually increasing, which affects the technology. Stable play and application

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  • Multi-b-value diffusion magnetic resonance imaging optimization method based on signal-to-noise ratio weighting

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[0033] Below in conjunction with accompanying drawing, the present invention is described in detail:

[0034] like figure 1 As shown, the present invention provides a kind of multi-b-value diffusion MRI optimization method based on SNR weighting, comprising the following steps:

[0035] In step one 101, a fitting model is established through multiple b-value weighted data; the fitting model in the step one establishes S i =f(b i , β), where β is the unknown parameter to be solved, S i >0,b i >0, i=0,1,2,3....

[0036] In practice, multi-b-weighted imaging is common in current MRI technology applications, and its data includes a non-diffusion-weighted reference signal (image) with b=0, and diffusion-weighted imaging signals with several non-zero b-values (image), and these non-zero b-value diffusion-weighted signals (images) are from the same subject. Here, the reference signal is set to have a strength of S 0 =S(b 0 ), while the weighted b-value signal is S i =S(b i...

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Abstract

The invention discloses a multi-b-value diffusion magnetic resonance imaging optimization method based on signal-to-noise ratio weighting. The method comprises the following steps of through multi-b-value weight data, establishing a fitting model; through the multi-b-value weight data, estimating a signal to noise ratio; according to the signal to noise ratio, calculating a residual error correction weight alpha i and establishing a fitting model initial value; according to the residual error correction weight alpha i, calculating a fitting model residual error correction value; if the fitting model residual error correction value accords with a convergence condition, through an optimal solution of the fitting model, calculating and increasing stability; otherwise, returning to a step4 and continuously seeking the optimal solution of the fitting model. In the method, aiming at multi-b-value diffusion weighting, the fitting model is constructed; through optimization of a least square algorithm, stability of model optimization searching solution is increased so that estimation of a parameter image in MRI imaging is stable; and generation of a singular value or a meaningless value is reduced so as to further promote development and application of a MRI imaging technology.

Description

technical field [0001] The invention relates to an optimization and improvement method of a nonlinear least squares algorithm in the application of MRI imaging technology, in particular to a multi-b value diffusion magnetic resonance imaging optimization method based on signal-to-noise ratio weighting. Background technique [0002] Diffusion magnetic resonance imaging technology based on multi-weighted b-values ​​has increasingly become the development trend of diffusion magnetic resonance technology, including diffusion kurtosis imaging and diffusion spectrum imaging. At the same time, the order of the model is also increasing, for example, from the second-order diffusion tensor imaging to the fourth-order diffusion kurtosis imaging model. Therefore, the sensitivity of the model to the signal-to-noise ratio of the data is also becoming more and more sensitive. Therefore, in practical applications, due to the singular value of the model fitting error, the fitting instability...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R33/56
CPCG01R33/5608
Inventor 陈元园赵欣沙淼张雄倪红艳明东
Owner TIANJIN UNIV
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