mlsanityData to 14 Oct 2026

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

arXiv 2606.10756 ↗First posted 9 Jun 2026cs.CV physics.med-phcited by 0

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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.

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References · 16

  1. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  2. 2021NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstructioncited by 146
  3. 2023Self-Supervised MRI Reconstruction with Unrolled Diffusion Modelscited by 38
  4. 2021Rethinking Positional Encodingcited by 37
  5. 2022Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstructioncited by 36
  6. 2022Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imagingcited by 35
  7. 2020Deep Low-rank plus Sparse Network for Dynamic MR Imagingcited by 32
  8. 2021Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Frameworkcited by 26
  9. 2025Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstructioncited by 4
  10. 2024Across-subject ensemble-learning alleviates the need for large samples for fMRI decodingcited by 3
  11. 2025Physics-Informed Implicit Neural Representations for Joint B0 Estimation and Echo Planar Imagingcited by 3
  12. 2025Self-supervised feature learning for cardiac Cine MR image reconstructioncited by 3
4 more
  1. 2023Non-Cartesian Self-Supervised Physics-Driven Deep Learning Reconstruction for Highly-Accelerated Multi-Echo Spiral fMRIcited by 2
  2. 2024Topological Cycle Graph Attention Network for Brain Functional Connectivitycited by 1
  3. 2024SNAKE-fMRI: A modular fMRI data simulator from the space-time domain to k-space and backcited by 1
  4. 2024ACCELERATION: Sequentially-scanning DECT Imaging Using High Temporal Resolution Image Reconstruction And Temporal Extrapolationcited by 1

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