Simultaneous Tri-Modal Medical Image Fusion and Super-Resolution using Conditional Diffusion Model

TL;DR

This paper introduces TFS-Diff, a novel diffusion model-based method that simultaneously performs tri-modal medical image fusion and super-resolution, improving image quality and integration for better clinical diagnosis.

Contribution

The paper presents TFS-Diff, the first model to jointly address tri-modal medical image fusion and super-resolution using diffusion processes and a new fusion-super-resolution loss.

Findings

Outperforms existing methods in quantitative metrics

Achieves superior visual quality in fused images

Effectively integrates multi-modal information for diagnosis

Abstract

In clinical practice, tri-modal medical image fusion, compared to the existing dual-modal technique, can provide a more comprehensive view of the lesions, aiding physicians in evaluating the disease's shape, location, and biological activity. However, due to the limitations of imaging equipment and considerations for patient safety, the quality of medical images is usually limited, leading to sub-optimal fusion performance, and affecting the depth of image analysis by the physician. Thus, there is an urgent need for a technology that can both enhance image resolution and integrate multi-modal information. Although current image processing methods can effectively address image fusion and super-resolution individually, solving both problems synchronously remains extremely challenging. In this paper, we propose TFS-Diff, a simultaneously realize tri-modal medical image fusion and super-resolution model. Specially, TFS-Diff is based on the diffusion model generation of a random iterative denoising process. We also develop a simple objective function and the proposed fusion super-resolution loss, effectively evaluates the uncertainty in the fusion and ensures the stability of the optimization process. And the channel attention module is proposed to effectively integrate key information from different modalities for clinical diagnosis, avoiding information loss caused by multiple image processing. Extensive experiments on public Harvard datasets show that TFS-Diff significantly surpass the existing state-of-the-art methods in both quantitative and visual evaluations. Code is available at https://github.com/XylonXu01/TFS-Diff.