\section{Related Work}
\label{app:related}
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\paragraph{Neural Fields}
Neural Fields \citep{xie2022neural} model signals as continuous neural functions that map from some domain to the corresponding signal value. Recent advances in NFs have mainly focused on alleviating the spectral bias \cite{rahaman2019spectral} of commonly used MLPs. A variety of different strategies evolved, ranging from the application of Fourier Features in ReLU MLPs \cite{tancik2020fourier}, over periodic sinusoidal activation functions like SIREN \cite{sitzmann2020implicit}, to using Gabor wavelet-based nonlinearities like WIRE \cite{saragadam2023wire}, or multi-resolution hash-grid encodings like in InstantNGP \cite{muller2022instant}. Research not only focused on network architectures and activation functions, but also explored training strategies like soft mining \cite{kheradmand2024accelerating}, or context-pruning \cite{tack2023learning} that both aim to identify informative samples, as well as network initialization strategies \cite{yeomfast2025,kaniafresh2025}. In the medical field, single-instance NFs have become particularly relevant in settings where data is scarce, irregularly sampled \cite{huang2023neural}, or should be free of cohort priors \cite{mcginnis2023single}. Applications include slice-to-volume and unsupervised dynamic MR reconstruction \cite{xu2023nesvor,feng2025mrrecon}, self-supervised motion correction \cite{al2024moco}, sparse-view CBCT reconstruction \cite{zha2022naf}, and image registration \cite{wolterink2022implicit,sideri2024sinr}.
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\paragraph{Generalizable Neural Fields}
Several approaches have been proposed to generalize NFs from single instances to entire datasets. Earlier methods like SIREN \cite{sitzmann2020implicit}, GASP \cite{dupont2021generative}, COIN \cite{dupont2021coin}, or HyperDiffusion \cite{erkocc2023hyperdiffusion} rely on first fitting a set of single-instance NFs, and subsequently using the resulting weights for solving downstream tasks like generation or compression. This is not only expensive, but the unordered nature of the constructed weight spaces also limits the performance of these methods. To bypass first-stage, single-instance NF fitting, auto-decoder frameworks \cite{park2019deepsdf,stolt2023nisf,bieder2024modeling} jointly optimize a shared network and signal-specific latent vectors. Representative methods such as DeepSDF \cite{park2019deepsdf}, CINA \cite{dannecker2024cina} and NISF \cite{stolt2023nisf} follow this strategy, but their naive joint optimization makes fitting new latents at test time slow and computationally expensive. To overcome this limitation, frameworks that \emph{learn-to-learn}, i.e., train the shared network in a way that fitting a new latent at test time requires few steps only, arose. Methods like MetaSDF \cite{sitzmann2020metasdf}, COIN++ \cite{dupont2022coin++}, Functa \cite{dupont2022data}, SpatialFuncta \cite{bauer2023spatial} or LIFT \cite{kazerouni2025lift} use different meta-learning strategies to achieve this goal. A parallel line of research aims to train hypernetworks that generate NF weights, without the need for first-stage single-instance NF fitting \cite{klocek2019hypernetwork,du2021learning,zhuang2023diffusion}. They do so by using the auto-decoder framework to learn hypernetworks that map from a low-dimensional, locally linear manifold to the desired NF weights \cite{du2021learning}, or by leveraging an explicit field parameterization and score field networks to learn distributions over neural fields in an end-to-end fashion \cite{zhuang2023diffusion}. 

While single-instance and generalizable NFs have been studied as a novel \emph{data modality} in the deep learning community \cite{dupont2022data,schurholt2022model,ma2024implicit}, their application to medical data, along with the unique challenges and opportunities, remains unexplored.