Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields

Area of Science:

• Neuroscience
• Biophysics
• Microscopy

Background:

• Adaptive optics (AO) corrects optical aberrations for improved microscopy but often requires custom setups and is sensitive to sample movement.
• Existing AO systems can be complex and may not perform optimally with commercial microscopes or in the presence of biological sample motion.

Purpose of the Study:

• To introduce NeAT, a computational framework for adaptive optics two-photon fluorescence microscopy.
• To develop a method for aberration correction and sample structure recovery that does not require external training datasets.
• To enable real-time, in vivo imaging in biological laboratories, including commercial microscopes.

Main Methods:

• NeAT utilizes neural fields to estimate wavefront aberrations and reconstruct 3D sample structure from image stacks.
• The framework incorporates motion correction and corrects conjugation errors specific to commercial microscopes.
• Performance was validated using both custom-built and commercial microscopes under various aberration and motion conditions.

Main Results:

• NeAT successfully estimates aberrations and recovers sample structure without prior training data.
• The system demonstrates effective real-time aberration correction in vivo using a commercial microscope.
• NeAT significantly improved signal and accuracy for glutamate and calcium imaging in the living mouse brain.

Conclusions:

• NeAT offers a versatile and deployable computational solution for adaptive optics microscopy.
• The framework enhances in vivo imaging capabilities by addressing aberrations and motion artifacts.
• NeAT improves the quality and reliability of functional and morphological imaging in neuroscience research.