Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models

TL;DR

This paper introduces a frequency-guided diffusion model (FGDM) for zero-shot medical image translation that preserves structural details without needing paired training data, outperforming existing methods across multiple imaging tasks.

Contribution

The proposed FGDM enables structure-preserving, zero-shot medical image translation using frequency-domain guidance, eliminating the need for paired source-target training data.

Findings

FGDM outperforms state-of-the-art methods in FID, PSNR, and SSIM metrics.

It achieves high-quality, structure-preserving translation across various medical imaging modalities.

FGDM demonstrates robustness and generalization in cross-institutional and multi-site tasks.

Abstract

Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images. For instance, errors in image translation may distort, shift, or even remove structures and tumors, leading to incorrect diagnosis and inadequate treatments. Training and conditioning diffusion models using paired source and target images with matching anatomy can help. However, such paired data are very difficult and costly to obtain, and may also reduce the robustness of the developed model to out-of-distribution testing data. We propose a frequency-guided diffusion model (FGDM) that employs frequency-domain filters to guide the diffusion model for structure-preserving image translation. Based on its design, FGDM allows zero-shot learning, as it can be trained solely on the data from the target domain, and used directly for source-to-target domain translation without any exposure to the source-domain data during training. We evaluated it on three cone-beam CT (CBCT)-to-CT translation tasks for different anatomical sites, and a cross-institutional MR imaging translation task. FGDM outperformed the state-of-the-art methods (GAN-based, VAE-based, and diffusion-based) in metrics of Frechet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), showing its significant advantages in zero-shot medical image translation.