Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS
Journal:
arXiv
Published Date:
Mar 3, 2025
Abstract
Hallucinations are spurious structures not present in the ground truth,
posing a critical challenge in medical image reconstruction, especially for
data-driven conditional models. We hypothesize that combining an unconditional
diffusion model with data consistency, trained on a diverse dataset, can reduce
these hallucinations. Based on this, we propose DynamicDPS, a diffusion-based
framework that integrates conditional and unconditional diffusion models to
enhance low-quality medical images while systematically reducing
hallucinations. Our approach first generates an initial reconstruction using a
conditional model, then refines it with an adaptive diffusion-based inverse
problem solver. DynamicDPS skips early stage in the reverse process by
selecting an optimal starting time point per sample and applies Wolfe's line
search for adaptive step sizes, improving both efficiency and image fidelity.
Using diffusion priors and data consistency, our method effectively reduces
hallucinations from any conditional model output. We validate its effectiveness
in Image Quality Transfer for low-field MRI enhancement. Extensive evaluations
on synthetic and real MR scans, including a downstream task for tissue volume
estimation, show that DynamicDPS reduces hallucinations, improving relative
volume estimation by over 15% for critical tissues while using only 5% of the
sampling steps required by baseline diffusion models. As a model-agnostic and
fine-tuning-free approach, DynamicDPS offers a robust solution for
hallucination reduction in medical imaging. The code will be made publicly
available upon publication.
