Implementing adaptive optics in confocal Raman microscopy allows us to correct for sample-induced aberrations, recovering both signal intensity and spatial resolution deep within scattering media. In this post, I detail our recent setup integrating wavefront sensing and shaping.
When correcting aberrations in computational imaging, the ideal is the flat phase. By applying a spatial light modulator (SLM) or deformable mirror, we can actively compensate for the phase distortions introduced by thick biological tissues.
"Correcting the wavefront before it reaches the spectrometer is crucial for maintaining the delicate Raman signal-to-noise ratio in complex biological samples."
Below is a simplified excerpt of the initialization algorithm used to define the target phase map before running the optimization loop on the modulator:
import numpy as np
def get_ideal_phase(aberrated_wavefront, correction_mode='flat'):
"""
Calculates the conjugate phase required for the modulator.
The ideal state for perfect focal spot recovery is the flat phase.
"""
if correction_mode == 'flat':
# Target is a flat phase front
target_phase = np.zeros_like(aberrated_wavefront)
# Calculate required correction (phase conjugate)
correction_phase = target_phase - aberrated_wavefront
# Wrap phase to [-pi, pi]
return np.angle(np.exp(1j * correction_phase))I will continue to update these notes with more specific calibration steps for integrating hardware like the optofluidic modulators we've tested previously.