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Abstract #0397

Spectral Wavelet-feature Analysis and Classification Assisted Denoising Approach for Enhancing Signal to Noise Ratios of MRS Data

Bing Ji1, Zahra Hosseini2, Liya Wang3, Lei Zhou4, Xinhua Tu5, and Hui Mao6
1Department of Radiology and Imaging Sciences, Emory University School of Medicine, Emory University, Atlanta, GA, Georgia, 2MR R&D Collaborations, Siemens Healthineers,, Atlanta, Georgia, 3Department of Radiology, The People’s Hospital of Longhua, Shenzhen, China, 4Department of Radiology and Imaging Sciences, Emory University School of Medicine, Emory University, Atlanta, Georgia, 5School of Communication and Information Engineering, Nanjing University of Posts and Telecommunication, Nanjing, China, 6Department of Radiology and Imaging Sciences, Emory University School of Medicine, Emory Univeristy, Atlanta, GA, United States

Low signal-to-noise ratio (SNR) and long acquisition time limit the clinical applications of magnetic resonance spectroscopy (MRS). This work presents a data-driven machine-learning assisted Spectral Wavelet-feature Analysis and Classification Assisted Denoising (SWANCAD) approach to extract the specific spectral wavelets of signals and noises for reducing noise and improving SNR of MRS data. The effective denoise by SWANCAD enabled resolving prominent metabolic peaks but also identify the smaller concentration metabolites which are merged in the noises. Potential applications of the SWANCAD includes the possibility of improving the signal to noise ratio (SNR) of MRS data collected in sub-minute or sub-cm voxels.

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