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

作者：Shu‐Fu Shih, Zhaohuan Zhang, Bilal Taşdelen, Ecrin Yağız, Sophia Cui, Xiaodong Zhong, Krishna S. Nayak, Holden H. Wu · 发表于：Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 年份：2024 · DOI：10.58530/2024/4175 · 被引用次数：1 · 研究领域：Advanced MRI Techniques and Applications、Photoacoustic and Ultrasonic Imaging、Atomic and Subatomic Physics Research

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.