Over the past few decades, the design of biomedical optics systems, image formation, and analysis has been predominantly guided by classical physical modeling and signal processing approaches. More recently, deep learning (DL) has emerged as a transformative approach in computational modeling, offering significant advantages across diverse scientific fields and data analysis challenges. Our lab has been at the forefront of developing DL methodologies for optical imaging, spanning a wide range of applications, including fluorescence lifetime imaging, fluorescence LiDAR imaging, 3D optical tomography, spectral and temporal unmixing for molecular imaging, and functional neuroimaging. These innovative methods address critical challenges such as denoising, image formation and reconstruction, estimation of tissue optical properties, prediction of psychomotor task performance, and subject classification. Our work, now a benchmark in the field, is advancing the capabilities of biomedical imaging, enhancing precision, and expanding potential applications in diagnostics and research.

Lead PI: Dr. Xavier Intes