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Abdullah M Aldokhail.pdf (2.16 MB)

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Abstract Header

Automated Signal to Noise Ratio Analysis for Magnetic Resonance Imaging Using a Noise Distribution Model

Aldokhail, Abdullah M
http://rave.ohiolink.edu/etdc/view?acc_num=mco1469557255

Abstract Details

, Master of Science in Biomedical Sciences (MSBS), University of Toledo, Biomedical Sciences (Medical Physics: Diagnostic Radiology).
Signal to noise ratio (SNR) is a fundamental index of image quality in Magnetic Resonance Imaging (MRI) and it has traditionally been considered as a sensitive test to monitor the performance of MRI systems. As part of quality assurance program, SNR measurement should have a high level of reproducibility and accuracy. Currently accepted methods to evaluate SNR rely upon manual analysis and are observer dependent. The goal of this project is to develop a robust automated method for SNR analysis to eliminate individual’s variability when performing such a test. This method uses a macro file that has been written using imageJ software to automatically segment the phantom from the background region, measure the mean signal from an area covering 80% of the phantom and determine the noise by fitting a noise distribution model to the background histogram. Fifty-four phantom scans from a variety of RF coils were used to compare the automated SNR analysis method with the manual SNR methods described in the ACR QC Manual. The automated SNR method proved to be accurate, highly reproducible, and relatively insensitive to the existence of minor artifacts in the noise measurement region.
E. Ishmael Parsai, Ph.D. (Committee Chair)
Kerry Krugh, Ph.D. (Committee Member)
Diana Shvydka, Ph.D. (Committee Member)
Medical Imaging

Recommended Citations

Citations

  • Aldokhail, A. M. (2016). Automated Signal to Noise Ratio Analysis for Magnetic Resonance Imaging Using a Noise Distribution Model [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=mco1469557255

    APA Style (7th edition)

  • Aldokhail, Abdullah. Automated Signal to Noise Ratio Analysis for Magnetic Resonance Imaging Using a Noise Distribution Model. 2016. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=mco1469557255.

    MLA Style (8th edition)

  • Aldokhail, Abdullah. "Automated Signal to Noise Ratio Analysis for Magnetic Resonance Imaging Using a Noise Distribution Model." Master's thesis, University of Toledo, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=mco1469557255

    Chicago Manual of Style (17th edition)

mco1469557255
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© 2016, all rights reserved.
This open access ETD is published by University of Toledo Health Science Campus and OhioLINK.