Physics-Informed Conditional Diffusion for Motion-Robust Retinal Temporal Laser Speckle Contrast Imaging

2026-04-22

AI Summary

Traditional retinal temporal laser speckle contrast imaging (tLSCI) requires long data sequences to achieve statistically stable estimates, rendering it susceptible to motion artifacts and limited in temporal resolution. This work proposes RetinaDiff, a novel framework that, for the first time, integrates physical priors into a conditional diffusion model and combines phase-correlation-based registration to enable motion-robust, structurally coherent, and statistically reliable blood flow reconstruction from only a few frames. Evaluated on a self-collected retinal LSCI dataset, the method substantially outperforms existing few-frame reconstruction approaches and baseline techniques, maintaining robust performance even under extremely low-frame-count conditions.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Feature Construction/Reformulation
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Retinal laser speckle contrast imaging (LSCI) is a noninvasive optical modality for monitoring retinal blood flow dynamics. However, conventional temporal LSCI (tLSCI) reconstruction relies on sufficiently long speckle sequences to obtain stable temporal statistics, which makes it vulnerable to acquisition disturbances and limits effective temporal resolution. A physically informed reconstruction framework, termed RetinaDiff (Retinal Diffusion Model), is proposed for retinal tLSCI that is robust to motion and requires only a few frames. In RetinaDiff, registration based on phase correlation is first applied to stabilize the raw speckle sequence before contrast computation, reducing interframe misalignment so that fluctuations at each pixel primarily reflect true flow dynamics. This step provides a physics prior corrected for motion and a high quality multiframe tLSCI reference. Next, guided by the physics prior, a conditional diffusion model performs inverse reconstruction by jointly conditioning on the registered speckle sequence and the corrected prior. Experiments on data acquired with a retinal LSCI system developed in house show improved structural continuity and statistical stability compared with direct reconstruction from few frames and representative baselines. The framework also remains effective in a small number of extremely challenging cases, where both the direct 5-frame input and the conventional multiframe reconstruction are severely degraded. Overall, this work provides a practical and physically grounded route for reliable retinal tLSCI reconstruction from extremely limited frames. The source code and model weights will be publicly available at https://github.com/QianChen113/RetinaDiff.

Problem

• retinal laser speckle contrast imaging
• motion robustness
• temporal resolution
• limited-frame reconstruction
• acquisition disturbance

Innovation

• Physics-Informed Diffusion
• Motion-Robust Imaging
• Retinal Blood Flow
• Temporal Laser Speckle Contrast Imaging
• Conditional Diffusion Model