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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