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arXiv · 2608.02324

Implicit Neural Representations for Multimodal Longitudinal Image Imputation and Interpolation

Sina Wendrich·Lukas Förner·Zoe Reinke·Kartikay Tehlan·Ansgar Berlis·Michael Frühwald·Matthias Wagner·Thomas Wendler
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Abstract

Longitudinal multiparametric MRI is central to follow-up imaging in oncology, yet real-world clinical data are characterised by missing sequences, heterogeneous acquisition protocols, and varying spatial resolutions across time points. We propose a patient-specific conditional implicit neural representation (INR) that models multimodal longitudinal MRI as a continuous function of world coordinates, time, and modality conditioning. The model is trained with stochastic modality dropout to handle incomplete data, and its continuous coordinate-space formulation enables both spatial and temporal interpolation without resampling to a fixed voxel grid. A self-consistency-based confidence estimator is derived from cross-modal reconstruction performance at inference time. We evaluate the framework on longitudinal MRI from paediatric brain tumour patients, demonstrating statistically significant improvements over linear interpolation for T1CE and FLAIR (p < 0.05), with mean MS-SSIM of 0.95 $\pm$ 0.02 for T1CE. Predicted confidence correlates strongly with true reconstruction quality (Pearson r up to 0.996), suggesting reliable deployment potential in heterogeneous clinical settings.

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Sina Wendrich, Lukas Förner, Zoe Reinke, Kartikay Tehlan, Ansgar Berlis, Michael Frühwald, Matthias Wagner, Thomas Wendler. 2026-08-03. Implicit Neural Representations for Multimodal Longitudinal Image Imputation and Interpolation. https://arxiv.org/abs/2608.02324

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