Review of MRI processing techniques and elaboration of a new two-parametric method of moments

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The paper provides a review of the existing methods of signals’ processing within the conditions of the Rice statistical model applicability. There are considered the principle development directions, the existing limitations and the improvement possibilities concerning the methods of solving the tasks of noise suppression and analyzed signals’ filtration by the example of magnetic-resonance visualization. A conception of a new approach to joint calculation of Rician signal’s both parameters has been developed based on the method of moments in two variants of its implementation. The computer simulation and the comparative analysis of the obtained numerical results have been conducted.

Keywords: Rice distribution, magnetic-resonance visualization, samples of measurements, mean value, noise dispersion
Citation in English: Yakovleva T.V. Review of MRI processing techniques and elaboration of a new two-parametric method of moments // Computer Research and Modeling, 2014, vol. 6, no. 2, pp. 231-244
Citation in English: Yakovleva T.V. Review of MRI processing techniques and elaboration of a new two-parametric method of moments // Computer Research and Modeling, 2014, vol. 6, no. 2, pp. 231-244
DOI: 10.20537/2076-7633-2014-6-2-231-244
According to Crossref, this article is cited by:
  • Tatiana Victorovna Yakovleva. Theoretical substantiation of the mathematical techniques for joint signal and noise estimation at rician data analysis. // Computer Research and Modeling. 2016. — V. 8, no. 3. — P. 445. DOI: 10.20537/2076-7633-2016-8-3-445-473
  • Tatiana Victorovna Yakovleva. Signal and noise calculation at Rician data analysis by means of combining maximum likelihood technique and method of moments. // Computer Research and Modeling. 2018. — V. 10, no. 4. — P. 511. DOI: 10.20537/2076-7633-2018-10-4-511-523
  • Tatiana Victorovna Yakovleva. Signal and noise parameters determination at rician data analysis by method of moments of lower odd orders. // Computer Research and Modeling. 2017. — V. 9, no. 5. — P. 717. DOI: 10.20537/2076-7633-2017-9-5-717-728
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Citations: 10 (RSCI).

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International Interdisciplinary Conference "Mathematics. Computing. Education"