Abstract #5139

Deep learning-based residual-subtractive denoising with latent mapping of noise: Application to Z-spectra

Sajad Mohammed Ali¹, Nirbhay Yadav^2,3, Ronnie Wirestam¹, Peter van Zijl^2,3,4, Jannik Prasuhn^2,3,5,6,7, and Linda Knutsson^1,2,3

¹Medical Radiation Physics, Lund University, Lund, Sweden, ²F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ³Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁴Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁵Department of Neurology, University-Medical Center Schleswig-Holstein, Lübeck, Germany, ⁶Institute of Neurogenetics, University of Lübeck, Lübeck, Germany, ⁷8Center for Brain, Behavior, and Metabolism, University of Lübeck,, Lübeck,, Germany

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Software

Motivation: Noise is an inherent limitation for all digital imaging and CEST MRI is no exception. With the already small effect-size some signals in the Z-spectra might be completely overshadowed. Available denoisers are limited in the aspect of lost-signal recovery.

Goal(s): Proof-of-concept for a deep learning based denoising approach targeting the latent representation of noise.

Approach: A DL-approach for latent mapping of noise and subsequent residual-subtractive removal was developed to map the latent noise and a subsequent residual-subtractive approach was applied.

Results: The developed approach showed great promise with elevated performance compared to state-of-the-art in the field in terms of several quantitative measures.

Impact: Denoising is crucial for usability of Z-spectra from CEST-images. Our developed DL-based solution is designed to map noise in latent space and subsequently remove it in a residual fashion allowing for an increased potential to recover overshadowed signal in Z-spectra.

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