# I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling

arXiv: https://arxiv.org/abs/2405.14022 · HTML: https://mlsanity.com/paper/2405.14022/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 59 of its 112 references that are in the map.
- First posted on arXiv 22 May 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for medical image analysis](https://mlsanity.com/ssm/mamba-for-medical-image-analysis/)

## Builds on (most cited first, 30 of 59)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.13560/) (2024)
- [CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.03024/) (2021)
- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining](https://mlsanity.com/paper/2402.03302/) (2024)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018)
- [Adaptive Diffusion Priors for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2207.05876/) (2022)
- [Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network](https://mlsanity.com/paper/1904.12200/) (2019)
- [MedMamba: Vision Mamba for Medical Image Classification](https://mlsanity.com/paper/2403.03849/) (2024)
- [Deep MR to CT Synthesis using Unpaired Data](https://mlsanity.com/paper/1708.01155/) (2017)
- [Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis](https://mlsanity.com/paper/2002.05000/) (2020)
- [TransMed: Transformers Advance Multi-modal Medical Image Classification](https://mlsanity.com/paper/2103.05940/) (2021)

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

- [Mamba in Vision: A Comprehensive Survey of Techniques and Applications](https://mlsanity.com/paper/2410.03105/) (2024)
- [DenoMamba: A fused state-space model for low-dose CT denoising](https://mlsanity.com/paper/2409.13094/) (2024)
- [Physics-Driven Autoregressive State Space Models for Medical Image Reconstruction](https://mlsanity.com/paper/2412.09331/) (2024)
- [Regression is all you need for medical image translation](https://mlsanity.com/paper/2505.02048/) (2025)
- [MambaRecon: MRI Reconstruction with Structured State Space Models](https://mlsanity.com/paper/2409.12401/) (2024)
- [Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction](https://mlsanity.com/paper/2502.04521/) (2025)
- [A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling](https://mlsanity.com/paper/2409.13477/) (2024)
- [Interactive Gadolinium-Free MRI Synthesis: A Transformer with Localization Prompt Learning](https://mlsanity.com/paper/2503.01265/) (2025)
- [GAMBAS: Generalised-Hilbert Mamba for Super-resolution of Paediatric Ultra-Low-Field MRI](https://mlsanity.com/paper/2504.04523/) (2025)
- [Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching](https://mlsanity.com/paper/2601.15884/) (2026)
- [Meta-Entity Driven Triplet Mining for Aligning Medical Vision-Language Models](https://mlsanity.com/paper/2504.15929/) (2025)
- [FermatSyn: SAM2-Enhanced Bidirectional Mamba with Isotropic Spiral Scanning for Multi-Modal Medical Image Synthesis](https://mlsanity.com/paper/2505.07687/) (2025)
- [BronchoGAN: Anatomically consistent and domain-agnostic image-to-image translation for video bronchoscopy](https://mlsanity.com/paper/2507.01387/) (2025)
- [GLFC: Unified Global-Local Feature and Contrast Learning with Mamba-Enhanced UNet for Synthetic CT Generation from CBCT](https://mlsanity.com/paper/2501.02992/) (2025)
- [Fully Guided Neural Schr"odinger bridge for Brain MR image synthesis](https://mlsanity.com/paper/2501.14171/) (2025)
- [MM2CT: MR-to-CT translation for multi-modal image fusion with mamba](https://mlsanity.com/paper/2508.05476/) (2025)
- [K-Syn: K-space Data Synthesis in Ultra Low-data Regimes](https://mlsanity.com/paper/2509.06997/) (2025)
- [Flip Distribution Alignment VAE for Multi-Phase MRI Synthesis](https://mlsanity.com/paper/2510.02970/) (2025)
- [DINOv3-Guided Cross Fusion Framework for Semantic-aware CT generation from MRI and CBCT](https://mlsanity.com/paper/2511.12098/) (2025)
- [NeuroSSM: Multiscale Differential State-Space Modeling for Context-Aware fMRI Analysis](https://mlsanity.com/paper/2601.01229/) (2026)
- [Any-to-All MRI Synthesis: A Unified Foundation Model for Nasopharyngeal Carcinoma and Its Downstream Applications](https://mlsanity.com/paper/2602.08822/) (2026)
- [DSS-GAN: Directional State Space GAN with Mamba backbone for Class-Conditional Image Synthesis](https://mlsanity.com/paper/2603.17637/) (2026)
- [CLIMB: Controllable Longitudinal Brain Image Generation using Mamba-based Latent Diffusion Model and Gaussian-aligned Autoencoder](https://mlsanity.com/paper/2604.15611/) (2026)
- [Prob-BBDM: a Probabilistic Brownian Bridge Diffusion Model for MRI sequence image-to-image translation](https://mlsanity.com/paper/2606.24313/) (2026)
- [AA-ViT: Anatomically Aware Vision Transformer with Structural and Frequency Guidance for Contrast Enhanced Brain MRI Synthesis](https://mlsanity.com/paper/2607.07553/) (2026)
- [PredRA: Fast Medical Image Translation by Deterministic Component Extraction and Controlled Stochastic Refinement](https://mlsanity.com/paper/2609.31912/) (2026)

## Abstract (arXiv)

Multi-modal medical image synthesis involves nonlinear transformation of tissue signals between source and target modalities, where tissues exhibit contextual interactions across diverse spatial distances. As such, the utility of a network architecture in synthesis depends on its ability to express the broad set of contextual features in medical images. Convolutional neural networks (CNNs) offer high local precision at the expense of poor sensitivity to long-range context. While transformers promise to alleviate this issue, they suffer from an unfavorable trade-off between sensitivity to long- versus short-range context due to the intrinsic complexity of attention filters. To effectively capture contextual features while avoiding the complexitydriven trade-offs, here we introduce a novel multi-modal synthesis method, I2I-Mamba, based on the state space modeling (SSM) framework. Focusing on high-level representations across a hybrid residual architecture, I2I-Mamba leverages novel dual-domain Mamba (ddMamba) blocks for complementary contextual modeling in image and Fourier domains, while maintaining spatial precision with convolutional layers. Diverting from conventional raster-scan trajectories, ddMamba leverages novel SSM operators based on a spiral-scan trajectory to learn context with enhanced angular isotropy and radial coverage, and a channel-mixing layer to aggregate context across the channel dimension. Comprehensive demonstrations on multi-contrast MRI and MRI-CT protocols indicate that I2I-Mamba outperforms state-of-the-art CNNs, transformers and SSMs.