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Functional Magnetic Resonance Imaging (fMRI) data is a kind of widely usedfour-dimensional biomedical data, demanding effective compression butpresenting unique challenges for compression due to its intricate temporaldynamics, low signal-to-noise ratio, and complicated underlying redundancies.This paper reports a novel compression paradigm specifically tailored for fMRIdata based on Implicit Neural Representation (INR). The proposed approachfocuses on removing the various redundancies among the time series, including(i) conducting spatial correlation modeling for intra-region dynamics, (ii)decomposing reusable neuronal activation patterns, and using properinitialization together with nonlinear fusion to describe the inter-regionsimilarity. The above scheme properly incorporates the unique features of fMRIdata, and experimental results on publicly available datasets demonstrate theeffectiveness of the proposed method, surpassing state-of-the-art algorithms inboth conventional image quality evaluation metrics and fMRI downstream tasks.This work in this paper paves the way for sharing massive fMRI data at lowbandwidth and high fidelity.

A Compact Implicit Neural Representation for Efficient Storage of Massive 4D Functional Magnetic Resonance Imaging