Fast super-resolution optical fluctuation imaging using a transformer-optimized neural network

Super-resolution optical fluctuation imaging (SOFI) achieves super-resolution (SR) imaging through simple hardware configurations while maintaining biological compatibility.However, the realization of large field-of-view (FoV) SOFI imaging remains fundamentally limited by extensive temporal sampling demands.Although modern SOFI techniques accelerate the acquisition speed, their cumulant operator necessitates fluorophores with a high duty cycle and a high labeling density to ensure that sufficient blinking events are acquired, which severely limits the practical implementation.For this, we present a novel framework that resolves the trade-off in SOFI by enabling millisecond-scale temporal resolution while retaining all the merits of SOFI.We named the framework the transformer-based reconstruction of ultra-fast SOFI (TRUS), a novel architecture combining transformer-based neural networks with physics-informed priors in conventional SOFI frameworks.For biological specimens with diverse fluorophore blinking characteristics, our method enables reconstruction using only 20 raw frames and the corresponding widefield images, which achieves a 47-fold reduction in raw frames (compared to the traditional methods that require more than 1000 frames) and sub-200-nm spatial resolution capability.To demonstrate the high-throughput SR imaging ability of our method, we perform SOFI imaging on the microtubule within a millimeter-scale FoV of 1.0 mm 2 with total acquisition time of 3 min.These characteristics enable TRUS to be a useful highthroughput SR imaging alternative in challenging imaging conditions.