Neural Implicit k-space with Trainable Periodic Activation Functions for Cardiac MR Imaging

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.