Unsupervised Medical Image Translation with Adversarial Diffusion Models (2207.08208v3)

Abstract: Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images involves one-shot mapping through generative adversarial networks (GAN). Yet, GAN models that implicitly characterize the image distribution can suffer from limited sample fidelity. Here, we propose a novel method based on adversarial diffusion modeling, SynDiff, for improved performance in medical image translation. To capture a direct correlate of the image distribution, SynDiff leverages a conditional diffusion process that progressively maps noise and source images onto the target image. For fast and accurate image sampling during inference, large diffusion steps are taken with adversarial projections in the reverse diffusion direction. To enable training on unpaired datasets, a cycle-consistent architecture is devised with coupled diffusive and non-diffusive modules that bilaterally translate between two modalities. Extensive assessments are reported on the utility of SynDiff against competing GAN and diffusion models in multi-contrast MRI and MRI-CT translation. Our demonstrations indicate that SynDiff offers quantitatively and qualitatively superior performance against competing baselines.

• The paper introduces SynDiff, an adversarial diffusion model that overcomes GAN limitations for high-fidelity medical image translation.
• It employs a bifurcated architecture with diffusive and non-diffusive modules and utilizes cycle-consistent learning on unpaired datasets.
• Numerical results demonstrate enhanced PSNR, SSIM, and perceptual quality, promising improved clinical imaging applications.

Overview of "Unsupervised Medical Image Translation with Adversarial Diffusion Models"

The paper introduces "SynDiff," an advanced adversarial diffusion model tailored for medical image translation. It addresses a critical task in medical imaging—synthesizing absent modalities solely based on available images, a process that enhances imaging diversity without additional patient cost or exposure. Traditional methods leverage one-shot transformations via Generative Adversarial Networks (GANs) but often fall short in sample fidelity. SynDiff leverages adversarial diffusion processes to achieve fast, high-fidelity translations while incorporating advancements that overcome GAN limitations like mode collapse and premature convergence.

SynDiff's architecture is bifurcated into diffusive and non-diffusive modules. The non-diffusive module implements initial source image estimations from target images using GAN components. These initial insights are pivotal for guiding the core diffusive module, which is underpinned by a novel adversarial diffusion process. This module goes beyond the traditional GAN constraints by adopting few large and efficient diffusive steps, significantly boosting sample credibility and reducing bias. Furthermore, SynDiff innovates through cycle-consistent learning for translation on unpaired datasets, a perennial challenge in unsupervised medical image synthesis.

Numerical Performance and Methodological Claims

SynDiff’s efficacy is quantitatively benchmarked against leading GAN and diffusion models across tasks involving multi-contrast MRI and MRI-CT translations. Experimental results demonstrate that SynDiff consistently surpasses alternative models, notably improving PSNR, SSIM, and perceptual quality measures. These numerical performance metrics substantiate the claim of SynDiff’s superior sample quality and fidelity, affirming its capability in translating medical images both quantitatively and qualitatively.

Implications and Future Directions

Practically, SynDiff offers a robust framework for clinical scenarios where multimodal imaging might be incomplete or infeasible. This is exceptionally pertinent in contexts requiring cost-effective, non-invasive, and comprehensive diagnostic protocols involving different imaging modalities such as MRI and CT. Theoretically, SynDiff's adversarial diffusion mechanism provides an effective paradigm shift, offering algorithmic insights that may influence the design of future generative models.

Exploring further, the architecture might integrate latent space representations and transformer-based networks for enhanced feature extraction and contextual awareness. Additionally, SynDiff's efficiency in handling unpaired datasets opens avenues for its application in medical domains with limited supervised data.

In summary, this paper contributes significant advancements in the automated synthesis of medical images, providing a pathway for future explorations and applications within radiology and beyond, especially concerning the unsupervised generation of clinically viable images that maintain anatomical accuracy and detail.