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.

翻译：视网膜激光散斑对比成像（LSCI）是一种用于监测视网膜血流动力学的无创光学成像方式。然而，传统的时序LSCI（tLSCI）重建依赖于足够长的散斑序列以获得稳定的时序统计量，这使其易受采集干扰的影响，并限制了有效的时间分辨率。本文提出了一种名为RetinaDiff（视网膜扩散模型）的物理信息重建框架，用于实现抗运动干扰且仅需少量帧的视网膜tLSCI重建。RetinaDiff中，首先基于相位相关性对原始散斑序列进行配准后再计算对比度，以减少帧间错位，使得每个像素的波动主要反映真实血流动力学变化。该步骤提供了经运动校正的物理先验与高质量多帧tLSCI参考。随后，在物理先验引导下，条件扩散模型通过联合作用于配准后的散斑序列及校正后的先验信息，实现逆重建。基于自主研发的视网膜LSCI系统采集数据的实验表明，相较于少量帧的直接重建及代表性基线方法，本方法在结构连续性和统计稳定性上均有提升。该框架在极少数极端困难案例中仍保持有效性——即使直接5帧输入与传统多帧重建均严重劣化。总体而言，本工作为基于极有限帧数的可靠视网膜tLSCI重建提供了兼具实用性与物理依据的路径。源代码与模型权重将在https://github.com/QianChen113/RetinaDiff 公开。