Adaptive optimization for axial multi-foci generation in multiphoton microscopy.

Area of Science:

• Biomedical optics
• Neuroscience imaging
• Microscopy

Background:

• Multifocal excitation enhances laser-scanning microscopy speed.
• Axial multifocal microscopy facilitates simultaneous multi-depth neuronal imaging in neuroscience.
• Existing methods produce uniform foci, failing to compensate for deep-tissue scattering and causing excitation inhomogeneity.

Purpose of the Study:

• To develop an adaptive phase-searching method (AdaPS) for generating axial multi-foci with tunable intensities.
• To compensate for scattering-induced power loss in deep tissues for uniform excitation.
• To improve the fidelity of multi-depth imaging in scattering biological samples.

Main Methods:

• Proposed a novel adaptive optimization-based phase-searching method (AdaPS).
• Utilized Adaptive Moment Estimation (Adam) for stable convergence to optimal phase patterns.
• Validated the method through numerical simulations and in vivo experimental recordings.

Main Results:

• AdaPS generates axial multi-foci with arbitrary intensity modulations.
• Achieved optimal phase patterns with errors an order of magnitude lower than previous methods.
• Demonstrated uniform multi-depth imaging in scattering phantoms.
• Enabled high-fidelity multi-depth recordings of neural network dynamics in vivo.

Conclusions:

• AdaPS effectively compensates for scattering-induced power loss in deep tissues.
• The method provides uniform multi-depth excitation for improved imaging.
• AdaPS enhances the capability of axial multifocal microscopy for neuroscience research.