In a study published in Nanophotonics, Prof. YAO Baoli from Xi'an Institute of Optics and Precision Mechanics (XIOPM) of the Chinese Academy of Sciences developed the image-based interferometric focus sensing (IBIFS) technology, which vastly expands aberration correction coverage and enhances imaging quality, marking a significant advancement in deep-tissue microscopy.

In biomedical microscopy, the strong scattering of deep tissue and system aberrations severely limits image quality. While adaptive optics (AO) can compensate for wavefront distortions in two-photon microscopy, its effectiveness is constrained by the optical memory effect, allowing only localized aberration correction within small fields of view. This compromises imaging stability and coverage. 

Existing methods rely on the local signal feedback, resulting in inconsistent correction and limited field coverage. Moreover, traditional AO requires pausing imaging for wavefront optimization, making it unsuitable for real-time observation of dynamic biological samples.

To address these issues, researchers developed the IBIFS method, which employs a conjugate AO configuration, iteratively correcting wavefront distortions across the entire field of view by monitoring image quality metrics. Integrating image-based evaluation parameters with an interferometric focus sensing algorithm expands the effective correction region. 

Unlike traditional Zernike decomposition, IBIFS method optimizes the point spread function, making it better suited for deep-tissue aberration correction. This method further enhances imaging stability by enabling real-time aberration compensation. Besides, it utilizes a resonant scanning mirror to accelerate full-field acquisition, offering significantly higher imaging speed than conventional galvanometric scanners.

The effectiveness of IBIFS was validated through experiments using fluorescence beads and mouse brain slices. The results demonstrated that IBIFS delivers real-time full-field aberration correction, with image intensity increased by approximately 37%, substantially outperforming traditional region-of-interest-based methods.

“The IBIFS technology, with its potential for integration with other imaging techniques, is poised to significantly enhance the precision and efficiency of deep tissue imaging, which plays a crucial role in neuroscience,” said Prof. YAO Baoli.