MDiff-FMT: Morphology-aware Diffusion Model for Fluorescence Molecular Tomography with Small-scale Datasets

TL;DR

This paper introduces MDiff-FMT, a morphology-aware diffusion model that improves fluorescence molecular tomography reconstruction by leveraging a probabilistic diffusion process and structural priors, achieving state-of-the-art results on small datasets.

Contribution

The paper presents the first morphology-aware diffusion model for FMT that enhances morphological reconstruction without requiring large datasets.

Findings

Achieves high-fidelity morphological reconstruction in FMT.

Outperforms existing methods with state-of-the-art results.

Effective on small-scale datasets without large training data.

Abstract

Fluorescence molecular tomography (FMT) is a sensitive optical imaging technology widely used in biomedical research. However, the ill-posedness of the inverse problem poses a huge challenge to FMT reconstruction. Although end-to-end deep learning algorithms have been widely used to address this critical issue, they still suffer from high data dependency and poor morphological restoration. In this paper, we report for the first time a morphology-aware diffusion model, MDiff-FMT, based on denoising diffusion probabilistic model (DDPM) to achieve high-fidelity morphological reconstruction for FMT. First, we use the noise addition of DDPM to simulate the process of the gradual degradation of morphological features, and achieve fine-grained reconstruction of morphological features through a stepwise probabilistic sampling mechanism, avoiding problems such as loss of structure details that may occur in end-to-end deep learning methods. Additionally, we introduce the conditional fluorescence image as structural prior information to sample a high-fidelity reconstructed image from the noisy images. Numerous numerical and real phantom experimental results show that the proposed MDiff-FMT achieves SOTA results in morphological reconstruction of FMT without relying on large-scale datasets.