Abstract #4175

K-space Low-rankness Enabled Additive NoisE Removal (KLEANER) to Denoise Multi-Coil Multi-Contrast Low-Field MRI

Shu-Fu Shih^1,2, Zhaohuan Zhang^1,2, Bilal Tasdelen³, Ecrin Yagiz³, Sophia X. Cui⁴, Xiaodong Zhong^1,2, Krishna S. Nayak³, and Holden H. Wu^1,2

¹Department of Radiolodical Sciences, University of California Los Angeles, Los Angeles, CA, United States, ²Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States, ³Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States, ⁴MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States

Synopsis

Keywords: Image Reconstruction, Low-Field MRI, Denoising

Motivation: Low-field MRI is limited by the low signal-to-noise ratio (SNR). Multiple scan averages increase SNR but also increase the acquisition time. In applications that acquire multiple contrasts, such as quantitative imaging, acquisition time can be further prolonged.

Goal(s): To develop a multi-coil multi-contrast k-space denoising technique that can also be compatible with parallel imaging-accelerated datasets.

Approach: A low-rank block-Hankel matrix was constructed from the multi-dimensional k-space data, followed by optimal singular value shrinkage to suppress Gaussian noise.

Results: In a pilot cohort, the proposed method improved SNR by 1.6-fold and reduced standard deviations in quantitative maps in the liver.

Impact: The proposed k-space denoising technique effectively suppresses noise in multi-coil multi-contrast k-space data from low-field MRI and is compatible with parallel imaging-accelerated datasets. It can improve image quality and/or shorten the acquisition time for multi-contrast low-field MRI.

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