Abstract
In MRI reconstruction, neural implicit k-space (NIK) representation maps spatial frequencies to k-space intensity values using an MLP with periodic activation functions. However, the choice of hyperparameters for periodic activation functions is challenging and influences training stability. In this work, we introduce and study the effectiveness of trainable (non-)periodic activation functions for NIK in the context of non-Cartesian Cardiac MRI. Evaluated on 42 radially sampled datasets from 6 subjects, NIKs with the proposed trainable activation functions outperform qualitatively and quantitatively other state-of-the-art reconstruction methods, including NIK with fixed periodic activation functions.
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Haft, P.T., Huang, W., Cruz, G., Rueckert, D., Zimmer, V.A., Hammernik, K. (2024). Neural Implicit k-space with Trainable Periodic Activation Functions for Cardiac MR Imaging. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_26
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DOI: https://doi.org/10.1007/978-3-658-44037-4_26
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