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Abstract #1184

A quest for deep learning MR image reconstruction loss functions in k-space

Siying Xu1, Wenqi Huang2, Kerstin Hammernik3, Maarten Terpstra4,5, Daniel Rueckert2,3,6, Sergios Gatidis1,7, and Thomas Kuestner1
1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tübingen, Germany, 2Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany, 3School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, 4Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, UMC Utrecht, Utrecht, Netherlands, 5Department of Radiotherapy, UMC Utrecht, Utrecht, Netherlands, 6Department of Computing, Imperial College London, London, United Kingdom, 7Department of Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, K-space Loss Function, Optimiaztion

Motivation: Existing loss functions are not optimal for measuring similarity in k-space.

Goal(s): To develop an improved k-space loss function that accounts for the k-space characteristics.

Approach: We propose a magnitude-phase-energy loss (MPE-loss), which employs separated magnitude and phase losses to improve the accuracy of complex-valued predictions, along with an additional energy term to account for the overall k-space energy.

Results: The proposed MPE-loss provides a symmetric loss landscape for each complex k-space sample, creating a quasi-convex loss that facilitates convergence under variations in image brightness and contrast. Experiments demonstrate that MPE-loss outperforms other standard loss functions in k-space reconstruction.

Impact: We propose a loss function that is more suitable for k-space, taking into account its characteristics and enhancing the accuracy of k-space regression. This loss function can be applied to any tasks that require calibration of k-space similarity.

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Keywords