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Accelerated T1 and T2 Parameter Mapping and Data Denoising Methods for 3D Quantitative MRI

Zhao, Nan
http://orcid.org/0000-0003-0445-5267
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748540796138

Abstract Details

2020, PhD, University of Cincinnati, Arts and Sciences: Physics.
Fast imaging has long been a key direction of magnetic resonance imaging (MRI) research. Rapid imaging technology can not only shorten the measurement time, reducing the time and cost of scientific research, but also reduce patient burden to in clinical applications and reduce some errors caused by longer measurements. At present, some rapid imaging methods have been applied to clinical medicine and have achieved good results. We achieve relatively good experimental results by adjusting the parameters in the MRI sequence based on tissue parameters such as the longitudinal relaxation time, T1, and transverse relaxation time, T2. Common sequence parameters to control contrast are the flip angle, a, echo time (TE), repetition time (TR), or the addition of pre-pulses (inversion, saturation, etc). T1 mapping and T2 mapping are very common imaging methods in MRI. High- contrast T1 and T2 mapping can clearly distinguish different tissues, providing important reference data for histological study and research. The work proposed in this thesis involves combination of fast imaging and classic MRI methods, in order to develop a new mapping method for T1 and T2, so as to obtain a mapping with sufficient accuracy in as short a time as possible. The method we used in our experiment is one of the fastest current approaches: driven-equilibrium single-pulse observation of T1 or T2 (DESPOT1/T2) which is based on making multiple measurements using steady state sequences with TR < T1 or T2. In order to shorten the sampling time, we modified the DESPOT approach to use variable density sampling patterns that allow collecting as little data as possible while maintaining image quality. We also compared multiple image reconstruction methods to find the best experimental method, hoping to improve image quality. The image signal-to-noise ratio is usually adversely affected by a reduction of sampling data. Therefore, finding an accurate noise reduction method, regardless of the specific image acquisition approach is also one of our goals. We will introduce some of the most common noise reduction methods and compare them with a proposed joint multi-channel noise reduction. Because the data we collected are complex, the denoising methods we propose mainly aim at complex-valued volume data composed of multiple observations. The results show that the Bayesian least squares Gaussian scale mixtures (BLS-GSM) denoising method can provide a restored image with low noise and good preservation of image detail.
Gregory Lee, Ph.D. (Committee Chair)
Leigh Smith, Ph.D. (Committee Chair)
Scott Holland, Ph.D. (Committee Member)
David Mast, Ph.D. (Committee Member)
Jason Woods, Ph.D. (Committee Member)
112 p.
Radiology
fast imaging; DESPOT1T2 mapping; Quantitative MRI; multichannel denoising; Gaussian scale mixtures; Bayesian estimation

Recommended Citations

Citations

  • Zhao, N. (2020). Accelerated T1 and T2 Parameter Mapping and Data Denoising Methods for 3D Quantitative MRI [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748540796138

    APA Style (7th edition)

  • Zhao, Nan. Accelerated T1 and T2 Parameter Mapping and Data Denoising Methods for 3D Quantitative MRI. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748540796138.

    MLA Style (8th edition)

  • Zhao, Nan. "Accelerated T1 and T2 Parameter Mapping and Data Denoising Methods for 3D Quantitative MRI." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748540796138

    Chicago Manual of Style (17th edition)

ucin1613748540796138
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This open access ETD is published by University of Cincinnati and OhioLINK.