mlsanityData to 7 Oct 2026

MambaRecon: MRI Reconstruction with Structured State Space Models

arXiv 2409.12401 ↗First posted 19 Sep 2024eess.IV cs.CVcited by 7

On the map: State-space sequence models (S4, Mamba) → Mamba for medical image analysis

Abstract (arXiv)

Magnetic Resonance Imaging (MRI) is one of the most important medical imaging modalities as it provides superior resolution of soft tissues, albeit with a notable limitation in scanning speed. The advent of deep learning has catalyzed the development of cutting-edge methods for the expedited reconstruction of MRI scans, utilizing convolutional neural networks and, more recently, vision transformers. Recently proposed structured state space models (e.g., Mamba) have gained some traction due to their efficiency and low computational requirements compared to transformer models. We propose an innovative MRI reconstruction framework that employs structured state space models at its core, aimed at amplifying both long-range contextual sensitivity and reconstruction efficacy. Comprehensive experiments on public brain MRI datasets show that our model sets new benchmarks beating state-of-the-art reconstruction baselines. Code will be available (https://github.com/yilmazkorkmaz1/MambaRecon).

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 29 of its 58 references that are in the map. First posted on arXiv 19 Sep 2024.

Builds on

29 of 58 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling22 May
  2. 2024MedMamba: Vision Mamba for Medical Image Classification6 Mar
  3. 2024MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation28 Feb
  4. 2024Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation7 Feb
  5. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentation4 Feb
  6. 2024SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation24 Jan
  7. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation9 Jan

Other references · 22

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  2. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  3. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  4. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  5. 2024VMamba: Visual State Space Modelcited by 955
  6. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 935
  7. 2018Self-Attention Generative Adversarial Networkscited by 809
  8. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 384
  9. 2024MambaIR: A Simple Baseline for Image Restoration with State-Space Modelcited by 349
  10. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 201
  11. 2022On The Computational Complexity of Self-Attentioncited by 137
  12. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
10 more
  1. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  2. 2017Deep learning for undersampled MRI reconstructioncited by 91
  3. 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
  4. 2017A Deep Cascade of Convolutional Neural Networks for MR Image Reconstructioncited by 74
  5. 2022Swin Transformer for Fast MRIcited by 56
  6. 2018Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networkscited by 53
  7. 2022ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformercited by 52
  8. 2021Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstructioncited by 34
  9. 2022DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstructioncited by 30
  10. 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2024Physics-Driven Autoregressive State Space Models for Medical Image Reconstructioncited by 7
  2. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  3. 2025Efficient Vision Mamba for MRI Super-Resolution via Hybrid Selective Scanningcited by 1
  4. 2025FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstructioncited by 0
  5. 2025Mamba-Based Modality Disentanglement Network for Multi-Contrast MRI Reconstructioncited by 0
  6. 2026PAS-Mamba: Phase-Amplitude-Spatial State Space Model for MRI Reconstructioncited by 0
  7. 2026Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstructioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstruction
  2. 2024A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond
  3. 2026SO-Mamba: State-Ownership Mamba for Unrolled MRI Reconstruction
  4. 2024Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis
  5. 2024MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion
  6. 2024Vision Mamba: Cutting-Edge Classification of Alzheimer's Disease with 3D MRI Scans
  7. 2024Deform-Mamba Network for MRI Super-Resolution
  8. 2025GAMBAS: Generalised-Hilbert Mamba for Super-resolution of Paediatric Ultra-Low-Field MRI