# DD-INR: Dynamics-Driven Implicit Neural Representation for Accelerated Whole-Brain Functional MRI Reconstruction

arXiv: https://arxiv.org/abs/2606.10756 · HTML: https://mlsanity.com/paper/2606.10756/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 16 of 16)

- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction](https://mlsanity.com/paper/2108.10991/) (2021)
- [Self-Supervised MRI Reconstruction with Unrolled Diffusion Models](https://mlsanity.com/paper/2306.16654/) (2023)
- [Rethinking Positional Encoding](https://mlsanity.com/paper/2107.02561/) (2021)
- [Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction](https://mlsanity.com/paper/2301.00127/) (2022)
- [Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging](https://mlsanity.com/paper/2212.08479/) (2022)
- [Deep Low-rank plus Sparse Network for Dynamic MR Imaging](https://mlsanity.com/paper/2010.13677/) (2020)
- [Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework](https://mlsanity.com/paper/2109.12502/) (2021)
- [Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction](https://mlsanity.com/paper/2501.09049/) (2025)
- [Across-subject ensemble-learning alleviates the need for large samples for fMRI decoding](https://mlsanity.com/paper/2407.12056/) (2024)
- [Physics-Informed Implicit Neural Representations for Joint B0 Estimation and Echo Planar Imaging](https://mlsanity.com/paper/2503.00230/) (2025)
- [Self-supervised feature learning for cardiac Cine MR image reconstruction](https://mlsanity.com/paper/2505.23408/) (2025)
- [Non-Cartesian Self-Supervised Physics-Driven Deep Learning Reconstruction for Highly-Accelerated Multi-Echo Spiral fMRI](https://mlsanity.com/paper/2312.05707/) (2023)
- [Topological Cycle Graph Attention Network for Brain Functional Connectivity](https://mlsanity.com/paper/2403.19149/) (2024)
- [SNAKE-fMRI: A modular fMRI data simulator from the space-time domain to k-space and back](https://mlsanity.com/paper/2404.08282/) (2024)
- [ACCELERATION: Sequentially-scanning DECT Imaging Using High Temporal Resolution Image Reconstruction And Temporal Extrapolation](https://mlsanity.com/paper/2408.06163/) (2024)

## Abstract (arXiv)

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.