Functional Magnetic Resonance Imaging (fMRI) data is a widely used kind of four-dimensional biomedical data, which requires effective compression. However, fMRI compressing poses unique challenges due to its intricate temporal dynamics, low signal-to-noise ratio, and complicated underlying redundancies. This paper reports a novel compression paradigm specifically tailored for fMRI data based on Implicit Neural Representation (INR). The proposed approach focuses on removing the various redundancies among the time series by employing several methods, including (i) conducting spatial correlation modeling for intra-region dynamics, (ii) decomposing reusable neuronal activation patterns, and (iii) using proper initialization together with nonlinear fusion to describe the inter-region similarity. This scheme appropriately incorporates the unique features of fMRI data, and experimental results on publicly available datasets demonstrate the effectiveness of the proposed method, surpassing state-of-the-art algorithms in both conventional image quality evaluation metrics and fMRI downstream tasks. This work in this paper paves the way for sharing massive fMRI data at low bandwidth and high fidelity.

翻译：功能磁共振成像（fMRI）数据是一种广泛使用的四维生物医学数据，需要进行有效压缩。然而，由于fMRI数据具有复杂的时间动态特性、低信噪比以及内在冗余结构的复杂性，其压缩面临独特挑战。本文提出了一种基于隐式神经表示（INR）的、专门针对fMRI数据的新型压缩范式。该方法通过多种方式消除时间序列中的各类冗余，具体包括：(i) 对区域内部动态进行空间相关性建模，(ii) 分解可重复使用的神经元激活模式，以及(iii) 利用合适的初始化与非线性融合来描述区域间相似性。该方案恰当地融合了fMRI数据的独特特征，在公开数据集上的实验结果表明，所提方法在常规图像质量评价指标和fMRI下游任务中均超越了现有最优算法。本文工作为以低带宽高保真方式共享海量fMRI数据铺平了道路。