Accelerated acquisition of fMRI enables enhanced detection of neurovascular (BOLD) activity in the brain, but image reconstruction becomes challenging with high k-space undersampling: Task-evoked BOLD signals are small in magnitude, which traditional anatomical MRI reconstruction methods fail to recover, as they favor spatial accuracy over temporal fidelity. We present DD-INR, a Dynamics-Driven Implicit Neural Representation framework tailored for accelerated fMRI that benefits from incoherent time-varying sampling and a tailored spatiotemporal prior, outperforming traditional methods, demonstrated in simulation and in-vivo acquisition, both in terms of image quality and retrieval of activation patterns. DD-INR achieves this by splitting the fMRI data into a static background and a temporally varying dynamic component, representing only the dynamics with a dedicated INR, thereby focusing the model's capacity on activation-relevant changes while remaining compact. In general, DD-INR provides a promising framework for accelerated fMRI reconstruction, with the potential to improve the sensitivity and robustness of fMRI studies within practical scan time limits. The source code is available at https://github.com/JoosenLi/DD-INR.

翻译：功能磁共振成像（fMRI）的加速采集能增强对脑内神经血管（BOLD）活动的检测，但高k空间欠采样下的图像重建面临挑战：任务诱发的BOLD信号幅度微小，传统解剖学MRI重建方法因偏重空间精度而非时间保真度而无法恢复。我们提出了DD-INR，一个专为加速fMRI设计的动力学驱动隐式神经表示框架，其受益于非相干时变采样和定制的时空先验，在模拟和活体采集中均优于传统方法，体现在图像质量和激活模式检索两方面。DD-INR通过将fMRI数据分解为静态背景和随时间变化的动态成分，仅使用专用INR表征动态部分，从而将模型能力聚焦于激活相关变化并保持紧凑性。总体而言，DD-INR为加速fMRI重建提供了富有前景的框架，有望在实际扫描时间限制内提高fMRI研究的灵敏度和鲁棒性。源代码可在https://github.com/JoosenLi/DD-INR获取。