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Abstract #0853

Magnetic Resonance Spectroscopic Imaging Data Denoising by Manifold Learning: An Unsupervised Deep Learning Approach

Amirmohammad Shamaei1,2, Jana Starčuková1, Radim Kořínek1, and Zenon Starčuk Jr1
1Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic, 2Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic


This work demonstrates an unsupervised deep-learning approach to MRS(I) data denoising, incorporating a non-linear model without relying on MR-theoretical physical prior knowledge. The implemented autoencoder learns the underlying low-dimensional manifold in high-dimensional data vectors representing MRS(I) voxels. The method, developed for denoising water-fat spectroscopic images, was tested against both numerical and acquired phantoms containing milk cream. The proposed method shows results comparable with other techniques in boosting SNR and has been found more robust in low concentration component denoising. Deep learning data denoising for MRSI might result in faster acquisition, vital for MRSI clinical application.

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