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Abstract #0311

Multi-Coil Multi-Contrast Random Matrix Theory-Based Denoising for Liver Fat and R2* Quantification at 0.55T

Shu-Fu Shih1,2, Zhaohuan Zhang1,2, Bilal Tasdelen3, Ecrin Yagiz3, Sophia X. Cui4, Xiaodong Zhong4, Krishna S. Nayak3, and Holden H. Wu1,2
1Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States, 2Bioengineering, University of California Los Angeles, Los Angeles, CA, United States, 3Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angles, CA, United States, 4MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angles, CA, United States

Synopsis

Keywords: Low-Field MRI, Low-Field MRI

Liver fat and R2* quantification has been extensively validated at 1.5T and 3T. Recently, there is renewed interest in lower-field MRI because of potential advantages such as a larger bore and lower costs. However, it is challenging to acquire images with sufficient signal-to-noise ratio for accurate fat and R2* quantification at 0.55T. Previous random matrix theory (RMT)-based approaches leverage Gaussian noise characteristics to markedly reduce the noise without compromising the tissue signal. In this work, we investigated a multi-coil multi-contrast RMT-based denoising approach which is compatible with parallel imaging and we demonstrated improved liver fat and R2* quantification at 0.55T.

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Abstract Presentation Video

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