@inproceedings{35088dc1d26c4b1091a8b831d59d81c1,
title = "Neural Implicit k-space with Trainable Periodic Activation Functions for Cardiac MR Imaging",
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.",
author = "Haft, \{Patrick T.\} and Wenqi Huang and Gastao Cruz and Daniel Rueckert and Zimmer, \{Veronika A.\} and Kerstin Hammernik",
note = "Publisher Copyright: {\textcopyright} Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2024.; German Conference on Medical Image Computing, BVM 2024 ; Conference date: 10-03-2024 Through 12-03-2024",
year = "2024",
doi = "10.1007/978-3-658-44037-4\_26",
language = "English",
isbn = "9783658440367",
series = "Informatik aktuell",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "82--87",
editor = "Andreas Maier and Deserno, \{Thomas M.\} and Heinz Handels and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff",
booktitle = "Bildverarbeitung f{\"u}r die Medizin 2024 - Proceedings, German Conference on Medical Image Computing, 2024",
}