AI-driven dual-mode phase and label-free fluorescence imaging platform using a single-shot gabor hologram
Abstract
Simultaneous acquisition of quantitative phase and fluorescence information is essential for comprehensive cellular analysis, as these complementary modalities provide structural and biochemical insights, respectively. However, conventional dual-mode imaging systems require fluorescent labeling, complex optical architectures, and multiple acquisition channels, which limit scalability, increase cost, and hinder deployment in low-resource or high-throughput settings. Here, we present an AI-driven dual-mode phase and label-free fluorescence imaging platform using a single-shot Gabor hologram. The proposed framework enables simultaneous reconstruction of quantitative phase images and virtual fluorescence channels from a single low-cost holographic measurement, eliminating the need for fluorescent staining, multi-shot acquisition, or multimodal optical hardware. To achieve this, we introduce a one-sided unsupervised diffusion model that learns a unidirectional mapping from Gabor holograms to dual-mode outputs without requiring paired training data or cycle-consistency constraints. The model integrates contrastive learning-based hologram synthesis with hologram-conditioned denoising diffusion to ensure high structural fidelity and robust cross-modal reconstruction. Ground-truth phase and fluorescence images acquired from a conventional dual-mode optical system are used only during training, while inference relies exclusively on a single-shot Gabor hologram captured with a minimal…
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