Resolution-enhanced quantitative phase imaging of blood platelets using a generative adversarial network

We developed a new method to enhance the resolution of blood platelet aggregates imaged via quantitative phase imaging (QPI) using a Pix2Pix generative adversarial network (GAN). First, 1 µm polystyrene beads were imaged with low- and high-resolution QPI, to train the GAN model and validate its applicability. Testing on the polystyrene beads demonstrated a mean error of 4.14% in the generated high-resolution optical-path-delay values compared to the optically acquired ones. Next, blood platelets were collected with low- and high-resolution QPI, and a deep neural network was trained to predict the high-resolution platelet optical-path-delay profiles using the low-resolution profiles, achieving a mean error of 7.01% in the generated high-resolution optical-path-delay values compared to the optically acquired ones. These results highlight the potential of the method in enhancing QPI resolution of cell aggregates without the need for sophisticated optical equipment and optical system modifications for high-resolution microscopy, allowing for better understanding of platelet-related disorders and conditions such as thrombocytopenia and thrombocytosis.