Abstract #1366

Explicit network noise amplification penalty in loss function for k-space interpolation networks through fast backpropagation

Istvan Homolya¹, Peter Dawood², Jannik Stebani³, and Martin Blaimer³

¹Molecular and Cellular Imaging, Comprehensive Heart Failure Center, University Hospital Würzburg, Würzburg, Germany, ²Department of Physics, University of Würzburg, Würzburg, Germany, ³Magnetic Resonance and X-ray Imaging Department, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Würzburg, Germany

Synopsis

Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence

Motivation: GRAPPA and RAKI optimize purely for data consistency, completely lacking physics-driven or model-based loss terms.

Goal(s): Recurrently feed noise amplification information into k-space interpolation networks by penalizing the online computed g-factor.

Approach: JAX-implemented GRAPPA and RAKI g-factors were estimated online in each training iteration and incorporated into the optimization as an inherent network noise amplification penalty.

Results: Networks including g-factor loss outperformed implementations optimizing only for the data consistency term. Inclusion of g-factor loss terms manifested Tikhonov regularization-like effects on image noise distribution, as revealed by difference maps to the fully sampled gold standard.

Impact: Incorporating the penalty of inherent noise amplification into k-space interpolation networks reduces reconstruction noise levels compared to implementation that optimize only for data consistency. G-factor-informed reconstructions manifest Tikhonov regularization-like effects, as revealed by noise distribution on difference maps.

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