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Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS

Official implementation of the MICCAI 2025 (Early Accept, Top 9%) paper

Status: Code under active development

Paper   |   Contact Author

Overview

Hallucinations, spurious structures not present in ground truth, pose a critical challenge in medical image reconstruction, particularly for data-driven conditional models. Our work investigates this phenomenon and introduces DynamicDPS, an innovative approach designed to mitigate hallucination while improving reconstruction fidelity and efficiency.

Method Overview

The schematic below illustrates our method (DynamicDPS) in comparison to traditional approaches. DynamicDPS achieves faster inference and avoids hallucination, outperforming standard conditional and diffusion models.

Schematic overview: DynamicDPS vs. traditional approaches

Visual Comparisons

Below: Visual comparisons on REAL low-field MR scans. DynamicDPS demonstrates superior reconstruction quality with fewer hallucinated features.

Visual comparisons on real low-field MR scans

Usage

Note: The final cleaned-up version of the code will be released soon.

You need to unzip motionblur.zip first

Download the pretrained model here -> LINK

Training the Score-Matching Model

python image_train.py

Solve Inverse Problems (e.g., Low-Field MRI Enhancement)

python test.py

Citation

If you find this work useful, please consider citing:

@InProceedings{KimSeu_Tackling_MICCAI2025,
        author = { Kim, Seunghoi AND Tregidgo, Henry F. J. AND Figini, Matteo AND Jin, Chen AND Joshi, Sarang AND Alexander, Daniel C.},
        title = { { Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
        year = {2025},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15963},
        month = {September},
        page = {593 -- 603}
}