Vision science - driven multimodal and computational imaging

Label-free modalities are ideal for clinical imaging as they do not require exogenous contrast agents, resulting in low chemical and phototoxicity. Our most recent effort involves the development of low-cost optical coherence tomography (OCT) devices to measure the axial eye length and track myopia progression in pediatric patients, through inverse problem solving or machine learning. 

 While fluorescence microscopy and label-free imaging excel in their respective applications, bridging preclinical and clinical studies remains a challenge. To address this, we employ artificial intelligence to enhance label-free imaging with the highly specific and quantitative fluorescence data collected from the same animals. This AI-empowered label-free technique can then be translated to humans, ultimately bridging the gap between preclinical and clinical settings.