One AI model to fix them all: foundation network restores seven types of medical images

Medical imaging is the bedrock of modern diagnosis, yet the pictures clinicians actually work with are rarely pristine. Computed tomography scans are degraded by metal artefacts from implants, magnetic resonance images suffer from patient motion and undersampling, positron emission tomography is inherently noisy at low radiation doses, and endoscopic, ultrasound, fundus and X-ray images each carry their own characteristic flaws. A team of researchers in China now reports a single artificial intelligence model, called AMIFound, that can restore and enhance all seven of these imaging modalities at once, and its performance suggests that one well-designed foundation model can outperform dozens of specialist systems built for individual tasks.

The study, published in Nature Machine Intelligence by Luyang Cao, Zhijian Wu and colleagues at Nanjing University, Westlake University, Southeast University and the Shenzhen Institute of Advanced Technology, was trained and evaluated on an extraordinary corpus: 3.76 million paired images, in which each degraded clinical image is matched with a high-quality reference. The data were assembled from publicly available repositories including The Cancer Imaging Archive, OpenNeuro, NITRC, Grand Challenge, Synapse, Kaggle and GitHub, and span 34 MRI datasets covering brain, cardiac, abdominal and prostate imaging, 37 CT datasets ranging from thoracic to whole-body acquisitions, six X-ray datasets, five endoscopy datasets, four fundus photography datasets, two ultrasound datasets and one whole-body PET dataset for oncological imaging.

Architecturally, AMIFound follows a philosophy that has transformed natural-language processing but has been slower to arrive in medical image restoration: a shared backbone with adaptive, task-specific routing. A single encoder first maps any low-quality input, regardless of modality, into a common latent feature space that captures both modality cues and the nature of the degradation. A dynamic routing mechanism then selects from a pool of specialised expert modules to construct a restoration pathway tailored to each individual image. The expert pool contains fast Fourier transform attention experts that operate in the frequency domain, spatial attention experts, convolutional local-feature experts and hybrid FFT-CNN fusion experts, allowing the model to combine complementary frequency, spatial and structural information on the fly.

Training such a system required a way to generate realistic degradation across modalities. The researchers built a modality-aware degradation engine that transforms high-quality images into degraded observations using physics-informed simulations. For CT, metal artefacts are synthesised in the projection domain through random metal masks, Radon transformation, nonlinear artefact mapping and inverse reconstruction. MRI aliasing and low-resolution effects are produced by k-space truncation followed by inverse Fourier reconstruction, while PET low-dose degradation is simulated with Poisson noise, Gaussian noise and blur. Endoscopic low-light conditions are modelled by gamma correction and brightness reduction in HSV colour space, X-ray blur by cascaded filtering with anisotropic diffusion, ultrasound artefacts by reverberation echoes, geometric shadowing and depth attenuation, and fundus artefacts by random circular highlights and Gaussian softening. In total, the model handles seven representative degradation patterns.

The benchmark results are striking. Across all seven modalities, AMIFound outperformed 38 competing baselines, including both task-specific restoration networks and generalist models such as AMIR, MoCE-IR and Found-IR, on full-reference image quality metrics such as structural similarity and learned perceptual measures. Radar plots of structural similarity improvements show consistent gains over each competing framework, and detailed per-task comparisons show the model achieving the best or near-best scores against modality-specific methods for every restoration task, from CT metal artefact reduction to PET denoising and cataract-affected fundus enhancement.

Quantitative metrics alone do not convince radiologists, so the team also ran a subjective evaluation with clinicians across all seven modalities. Physicians rated the restored images on sharpness, artefact suppression, naturalness and diagnostic confidence. AMIFound achieved the highest or near-highest average scores in every modality and provided greater diagnostic confidence than competing methods. The model was also stress-tested under combined degradations, where multiple corruptions strike the same image simultaneously, and on naturally degraded clinical images rather than synthetic ones, maintaining its advantage in both settings and preserving anatomical structures and realistic intensity distributions where competitors tended to oversmooth or distort contrast.

Perhaps the most clinically consequential finding is what happens downstream. The researchers fed AMIFound-enhanced images into 44 disease detection tasks and found consistent improvements over detection on the original degraded images. The benefits extended to tumour stage prediction in non-small-cell lung cancer and to survival risk stratification, including improved individual-level prognostic ranking. Attention heatmaps from the CT-based tumour-stage classifier reveal a plausible mechanism: before enhancement, model attention is often diffuse or mislocalized and predictions are less certain, whereas after enhancement, attention concentrates on tumours, lung-parenchyma abnormalities and lesion boundaries, and the highest prediction probability more often agrees with the ground-truth stage.

The significance of this work lies partly in its economics. Hospitals generate enormous volumes of routine imaging that falls short of research-grade quality, and low-dose protocols, deliberately chosen to reduce radiation exposure, or motion artefacts from patients who cannot hold still, are unavoidable facts of clinical life. Previous approaches required a separate, carefully tuned network for each modality-degradation combination, an unsustainable proliferation as imaging technology diversifies. A single foundation model that generalises across modalities and degradations, trained once and deployed everywhere, could lower the barrier for smaller institutions and make image enhancement a routine preprocessing step for any AI diagnostic pipeline.

The authors have released their training and evaluation code publicly on GitHub, with a versioned archive on Zenodo, built on PyTorch and drawing on data-loader pipelines from prior all-in-one restoration frameworks such as PromptIR, AirNet and MoCE-IR. All datasets used were publicly available and accessed under their respective licences, and the team reports that the model preserves clinically relevant information rather than hallucinating plausible-looking detail, a critical concern for any generative approach in medicine. Funded by the National Natural Science Foundation of China and several regional programmes, the work positions AMIFound as a domain-specific foundation model for the unglamorous but essential task of making everyday clinical images good enough, not just for human eyes, but for the growing fleet of AI systems that depend on them.

Subject of Research: A multimodal foundation model for restoring and enhancing degraded medical images across seven imaging modalities

Article Title: An all-in-one foundation model for multimodal medical image restoration and enhancement

Article References: An all-in-one foundation model for multimodal medical image restoration and enhancement. (n.d.). https://doi.org/10.1038/s42256-026-01310-z

Image Credits: AI Generated

DOI: 10.1038/s42256-026-01310-z

Keywords: medical imaging, foundation model, image restoration, deep learning, AMIFound, CT, MRI, PET, ultrasound, endoscopy, lung cancer, diagnostic AI