Adaptive optimization for axial multi-foci generation in multiphoton microscopy

To improve imaging speed, multifocal excitation is widely adopted as a parallel strategy in laser-scanning microscopy. Specifically, axial multifocal microscopy is popular in neuroscience as it enables functional imaging of neurons in multiple depths simultaneously. However, previous phase searching algorithms for axial multi-foci generation generally generate foci of uniform intensities, which cannot compensate the scattering-induced power loss in deep tissue and causes inhomogeneous excitation. Here, we propose a novel adaptive optimization-based phase-searching method (AdaPS) to generate axial multi-foci with arbitrary intensity modulations for scattering-induced loss compensation. By adopting Adaptive Moment Estimation (Adam) as the searching algorithm, our method could escape from unsatisfactory local minima and stably converge to the optimal phase pattern with errors at least an order of magnitude lower. We validate AdaPS through both numerical simulations and experiments and demonstrate that AdaPS could provide uniform multi-depth imaging in scattering phantom and enable high-fidelity multi-depth recordings of neural network dynamics in mouse brain in vivo.