Model-based unmixing of photoswitching optoacoustic tomography
Yan Liu, Doctoral Student at EPFL

Seminar • 2025-02-18

Abstract
Optoacoustic imaging combined with reversibly photoswitchable proteins has emerged as a promising technology for the high-sensitivity and multiplexed imaging of cells in live tissues in preclinical research. Carefully-designed illumination schedules drive photoswitching proteins between ON and OFF states, resulting in a modulation which allows to image these specific reporters and separate them from the non-modulated background. However, unmixing spatially-overlapping proteins with different kinetics in noisy environments remains a challenge. We propose a model-based variational framework to computationally unmix and image different species of photo-switching reporters using optoacoustic tomography. It is based on a detailed mathematical description of the photo-switching mechanism, which models how relevant physical parameters such as the kinetic constants and light fluence impact the switching signal. We introduce an algorithm that operates on images, as opposed to traditional pixelwise approaches. It takes the form of an iterative inversion combined with tailored L1 and total-variation regularization to increase the robustness to noise and to improve the unmixing quality. We show that our method is able to disentangle multiple spatially overlapping labels and to recover continuous maps of quantities of interest on controlled phantoms and mice experiments.