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Investigation of Multicomponent MRI Relaxation Data with Stochastic Contraction Fitting Algorithm

Russell, Mary Elizabeth
http://rave.ohiolink.edu/etdc/view?acc_num=osu1397482043

Abstract Details

2014, Master of Mathematical Sciences, Ohio State University, Mathematics.
Brain tissue can be identified and studied with Magnetic Resonance Imaging. Instead of relying only on the image, more advanced methods obtain quantitative measurements that describe specific tissues. Differential equations govern the shape of the magnetization curve, and the six unknown parameters of these equations are fit to the curves obtained from real data. The six parameter fit is performed numerically using a stochastic contraction algorithm. The stability of the results depend on the size of the original search space as well as additional factors. Numerical simulations were performed using this method and the results are concurrent with other literature results.
Gregory Baker, PhD (Advisor)
Petra Schmalbrock, PhD (Committee Member)
70 p.
Mathematics; Radiology

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Citations

  • Russell, M. E. (2014). Investigation of Multicomponent MRI Relaxation Data with Stochastic Contraction Fitting Algorithm [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397482043

    APA Style (7th edition)

  • Russell, Mary . Investigation of Multicomponent MRI Relaxation Data with Stochastic Contraction Fitting Algorithm. 2014. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1397482043.

    MLA Style (8th edition)

  • Russell, Mary . "Investigation of Multicomponent MRI Relaxation Data with Stochastic Contraction Fitting Algorithm." Master's thesis, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397482043

    Chicago Manual of Style (17th edition)

osu1397482043
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