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Abstract #1751

Rethinking complex image reconstruction: ⟂-loss for improved complex image reconstruction with deep learning

Maarten Terpstra1,2, Matteo Maspero1,2, Jan Lagendijk1, and Cornelis A.T. van den Berg1,2
1Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands, 2Computational Imaging Group for MR diagnostics & therapy, University Medical Center Utrecht, Utrecht, Netherlands

The \(\ell^2\) norm is the default loss function for complex image reconstruction. In this work, we investigate the behavior of the \(\ell^1\) and \(\ell^2\) loss functions for complex image reconstruction with non-complex-valued models. Simulations show that these norms assign a lower loss to reconstructions with lower magnitude, introducing an asymmetry in the loss function. To address this, we propose a new, symmetric loss function, and train deep learning models to show that the proposed loss function achieves better performance and faster convergence on complex image reconstruction tasks.

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