# DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training

arXiv: https://arxiv.org/abs/1810.00302 · HTML: https://mlsanity.com/paper/1810.00302/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2018.

## Built on by (most cited first, 26 of 26)

- [Adaptive Diffusion Priors for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2207.05876/) (2022)
- [Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning](https://mlsanity.com/paper/2103.02148/) (2021)
- [Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers](https://mlsanity.com/paper/2105.08059/) (2021)
- [Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction](https://mlsanity.com/paper/2301.00127/) (2022)
- [Federated Learning of Generative Image Priors for MRI Reconstruction](https://mlsanity.com/paper/2202.04175/) (2022)
- [A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise](https://mlsanity.com/paper/2205.10278/) (2022)
- [Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance](https://mlsanity.com/paper/2203.11178/) (2022)
- [Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction](https://mlsanity.com/paper/2309.00783/) (2023)
- [Deep Manifold Learning for Dynamic MR Imaging](https://mlsanity.com/paper/2104.01102/) (2021)
- [FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution](https://mlsanity.com/paper/2108.03920/) (2021)
- [SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging](https://mlsanity.com/paper/2208.03904/) (2022)
- [PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI](https://mlsanity.com/paper/2501.09403/) (2025)
- [Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution](https://mlsanity.com/paper/2211.11144/) (2022)
- [CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2306.11238/) (2023)
- [Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI](https://mlsanity.com/paper/2306.11977/) (2023)
- [Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers](https://mlsanity.com/paper/2204.01706/) (2022)
- [RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging](https://mlsanity.com/paper/2304.03483/) (2023)
- [Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems](https://mlsanity.com/paper/2309.09250/) (2023)
- [A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging](https://mlsanity.com/paper/2506.04116/) (2025)
- [SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging](https://mlsanity.com/paper/2104.05901/) (2021)
- [A Deep Learning Approach for Parallel Imaging and Compressed Sensing MRI Reconstruction](https://mlsanity.com/paper/2209.08807/) (2022)
- [Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance](https://mlsanity.com/paper/2209.10298/) (2022)
- [Universal Generative Modeling in Dual-domain for Dynamic MR Imaging](https://mlsanity.com/paper/2212.07599/) (2022)
- [k-t CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstruction](https://mlsanity.com/paper/2310.11050/) (2023)
- [Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction](https://mlsanity.com/paper/2506.09100/) (2025)
- [Mamba-Based Modality Disentanglement Network for Multi-Contrast MRI Reconstruction](https://mlsanity.com/paper/2512.19095/) (2025)

## Abstract (arXiv)

Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill-posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k-space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multisupervised network training technique is developed to constrain the frequency domain information and reconstruction results at different levels. The comparisons with classical k-t FOCUSS, k-t SLR, L+S and the state-of-the-art CNN-based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time.