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Dynamic imaging is essential for analyzing various biological processes but faces two main challenges: data incompleteness and computational burden. For many imaging systems, high frame rates and short acquisition times require severe undersampling, leading to data incompleteness. Multiple images may then be compatible with the data, thus requiring special techniques (regularization) to ensure uniqueness of the reconstruction. Computational and memory requirements are particularly burdensome for three-dimensional applications requiring high spatiotemporal resolution. Exploiting redundancies in the object's spatiotemporal features is key to addressing both challenges. This contribution investigates neural fields, or implicit neural representations, to model the sought-after dynamic object. Neural fields are a particular class of neural networks that represent the dynamic object as a continuous function of …