# Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction arXiv: https://arxiv.org/abs/2309.13839 ยท HTML: https://mlsanity.com/paper/2309.13839/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 13 of its 20 references that are in the map. - First posted on arXiv 25 Sep 2023. ## Builds on (most cited first, 13 of 13) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017) - [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021) - [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022) - [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021) - [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022) - [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018) - [End-to-End Variational Networks for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2004.06688/) (2020) - [PromptIR: Prompting for All-in-One Blind Image Restoration](https://mlsanity.com/paper/2306.13090/) (2023) - [A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift](https://mlsanity.com/paper/2206.10810/) (2022) - [HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction](https://mlsanity.com/paper/2203.08213/) (2022) - [CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction](https://mlsanity.com/paper/2309.10836/) (2023) - [MRI Reconstruction via Cascaded Channel-wise Attention Network](https://mlsanity.com/paper/1810.08229/) (2018) ## Built on by (most cited first, 17 of 17) - [Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI](https://mlsanity.com/paper/2403.10064/) (2024) - [HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss](https://mlsanity.com/paper/2410.03624/) (2024) - [UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction](https://mlsanity.com/paper/2502.14899/) (2025) - [DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model](https://mlsanity.com/paper/2507.13087/) (2025) - [GENRE-CMR: Generalizable Deep Learning for Diverse Multi-Domain Cardiac MRI Reconstruction](https://mlsanity.com/paper/2508.20600/) (2025) - [Enabling Ultra-Fast Cardiovascular Imaging Across Heterogeneous Clinical Environments with A Generalist Foundation Model and Multimodal Database](https://mlsanity.com/paper/2512.21652/) (2025) - [Towards Prospective Medical Image Reconstruction via Knowledge-Informed Dynamic Optimal Transport](https://mlsanity.com/paper/2505.17644/) (2025) - [KP-INR: A Dual-Branch Implicit Neural Representation Model for Cardiac Cine MRI Reconstruction](https://mlsanity.com/paper/2508.12147/) (2025) - [HierAdaptMR: Cross-Center Cardiac MRI Reconstruction with Hierarchical Feature Adapters](https://mlsanity.com/paper/2508.13026/) (2025) - [SDUM: A Scalable Deep Unrolled Model for Universal MRI Reconstruction](https://mlsanity.com/paper/2512.17137/) (2025) - [Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach](https://mlsanity.com/paper/2404.06941/) (2024) - [Physical foundations for trustworthy medical imaging: a review for artificial intelligence researchers](https://mlsanity.com/paper/2505.02843/) (2025) - [Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction](https://mlsanity.com/paper/2505.23872/) (2025) - [Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement](https://mlsanity.com/paper/2511.09366/) (2025) - [CRUNet-MR-Univ: A Foundation Model for Diverse Cardiac MRI Reconstruction](https://mlsanity.com/paper/2601.04428/) (2026) - [Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?](https://mlsanity.com/paper/2604.22557/) (2026) - [Enhancing Implicit Neural Representations with Image Feature Embedding for Unsupervised Cardiac Cine MRI Reconstruction](https://mlsanity.com/paper/2607.04069/) (2026) ## Abstract (arXiv) The key to dynamic or multi-contrast magnetic resonance imaging (MRI) reconstruction lies in exploring inter-frame or inter-contrast information. Currently, the unrolled model, an approach combining iterative MRI reconstruction steps with learnable neural network layers, stands as the best-performing method for MRI reconstruction. However, there are two main limitations to overcome: firstly, the unrolled model structure and GPU memory constraints restrict the capacity of each denoising block in the network, impeding the effective extraction of detailed features for reconstruction; secondly, the existing model lacks the flexibility to adapt to variations in the input, such as different contrasts, resolutions or views, necessitating the training of separate models for each input type, which is inefficient and may lead to insufficient reconstruction. In this paper, we propose a two-stage MRI reconstruction pipeline to address these limitations. The first stage involves filling the missing k-space data, which we approach as a physics-based reconstruction problem. We first propose a simple yet efficient baseline model, which utilizes adjacent frames/contrasts and channel attention to capture the inherent inter-frame/-contrast correlation. Then, we extend the baseline model to a prompt-based learning approach, PromptMR, for all-in-one MRI reconstruction from different views, contrasts, adjacent types, and acceleration factors. The second stage is to refine the reconstruction from the first stage, which we treat as a general video restoration problem to further fuse features from neighboring frames/contrasts in the image domain. Extensive experiments show that our proposed method significantly outperforms previous state-of-the-art accelerated MRI reconstruction methods.