Principal component dimension reduction value obtaining algorithm based on signal to noise ratio
A signal-to-noise ratio and principal component technology, applied in computing, computer components, medical science, etc., can solve problems such as trial and error, no calculation method, no theoretical guidance, etc.
Inactive Publication Date: 2018-02-02
上海三誉华夏基因科技有限公司
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[0005] At present, the method of selecting this percentage value is mainly trial and error, without theoretical guidance, and there is no specific calculation method
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[0052] The following describes a preferred embodiment of the present invention with reference to the accompanying drawings to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned herein.
[0053] Such as figure 1 As shown, a dimensionality reduction method based on the principal component analysis and independent component analysis of the signal-to-noise ratio of the clinical magnetic resonance spectrum LCModel includes the following main steps:
[0054]S1. Obtain the signal-to-noise ratio vector R from the LCModel software used in clinical magnetic resonance spectroscopy;
[0055] S2, deriving the relationship between the signal-to-noise ratio defined in the LCModel software and the conventionally defined signal-to-noise ratio;
[0056] S3. Calculating the information percentage value θ required f...
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The invention discloses a principal component dimension reduction value obtaining algorithm based on a signal to noise ratio, and the algorithm is specifically a dimension reduction value obtaining method based on the principal component analysis and independent component analysis of a clinic magnetic resonance spectrum LCModel signal to noise ratio. The algorithm mainly comprises the steps: obtaining a signal to noise vector R from LCModel software employed for the clinic magnetic resonance spectrum, deducing the relation between the signal to noise ratio defined in the LCModel software and the signal to a conventional defined noise ratio, calculating the information percentage ratio theta needed by the dimension reduction value obtaining of the independent component analysis and principal component analysis, and updating the information percentage value theta through the mean value of signal to noise vectors R; calculating the number k of main components; calculating the peak value of signal to noise vectors R, and determining a final dimension reduction value. For the independent component analysis and principal component analysis, the dimension reduction method based on the whole sample data set signal to noise ratio is proposed for the first time. The algorithm gives a mathematic relation between the signal to noise ratio defined in the LCModel software and the conventional signal to noise ratio, so the algorithm is suitable for the dimension reduction mathematic deduction of the independent component analysis and principal component analysis.
Description
technical field [0001] The invention relates to the field of clinical magnetic resonance spectroscopy, in particular to a method for dimensionality reduction and value acquisition based on principal component analysis and independent component analysis of the signal-to-noise ratio of the clinical magnetic resonance spectroscopy LCModel. Background technique [0002] Clinical magnetic resonance spectroscopy, as a non-invasive, non-invasive quantitative detection of tissue metabolites, can help detect human tissue damage and track disease progression, and even facilitate further research on drug efficacy; it can avoid the need for conventional magnetic resonance imaging Misdiagnosis of tumor development, leading to unnecessary surgery and increased patient suffering. Since metabolic changes are thought to precede structural changes, magnetic resonance spectroscopy is more likely than magnetic resonance imaging to provide dynamic metabolic markers in the early stages of disease...
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Login to View More IPC IPC(8): G06K9/00A61B5/055
CPCA61B5/055G06F2218/00
Inventor 郝洁
Owner 上海三誉华夏基因科技有限公司