High-Resolution Breast Diffusion Weighted Imaging with Improved Nyquist Ghost Correction and Simultaneous Multislice Imaging

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High-Resolution Breast Diffusion Weighted Imaging with Improved Nyquist Ghost Correction and Simultaneous Multislice Imaging

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2020-07

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Abstract

Diffusion-weighted imaging (DWI) is a quantitative MRI method that measures the apparent diffusion coefficient (ADC) of water molecules, which reflects cell density and serves as an indication of malignancy. Unfortunately, however, the clinical value of DWI is severely limited by the undesirable features in images that common clinical methods produce, including large geometric distortions, ghosting and chemical shift artifacts, and insufficient spatial resolution. Thus, in order to exploit information encoded in diffusion characteristics and fully assess the clinical value of ADC measurements, it is first imperative to achieve technical advancements of DWI. The purpose of this work is to improve DWI methods for breast imaging at 3 Tesla to robustly provide diffusion-weighted images and ADC maps with anatomical quality and resolution. This dissertation will first lay out the background information to provide clinical motivation for this work and explain the current standard in breast DWI, as well as some alternatives proposed throughout the literature. The main work of this project has two major parts: Nyquist ghost correction and the use of simultaneous multislice imaging (SMS) to achieve high resolution. Exploratory work was completed to characterize the Nyquist ghost in breast DWI, showing that, although the ghost is mostly linear, the three-line navigator is unreliable, especially in the presence of fat. A novel referenceless ghost correction, Ghost/Object minimization was developed that reduced the ghost in standard SE-EPI and advanced SMS. An advanced SMS method with axial reformatting (AR) is presented for high resolution breast DWI. In a reader study, AR-SMS was preferred by three breast radiologists compared to the standard SE-EPI and readout-segmented-EPI. Finally, future directions are suggested, including some preliminary work explored throughout this project.

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University of Minnesota Ph.D. dissertation. July 2020. Major: Biomedical Engineering. Advisor: Patrick Bolan. 1 computer file (PDF); x, 152 pages.

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McKay, Jessica. (2020). High-Resolution Breast Diffusion Weighted Imaging with Improved Nyquist Ghost Correction and Simultaneous Multislice Imaging. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/216361.

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