Geometric optimization method for a polarization state generator of a Mueller matrix microscope.

We propose a geometric optimization method combined with the Coulombic energy indicator that can uniformly distribute N polarization states on the Poincaré sphere. Based on this method, we investigate the optimal frames of a rotating polarizer and rotating quarter-wave plate (RPRQ)-based polarization state generator (PSG) at different numbers of modulations. We use the PSG on a dual DoFP polarimeter-based Mueller matrix microscope to measure standard samples and pathological sections for testing the performance of an optimized RPRQ. The experimental results show that this method can effectively restrain noise and improve measurement accuracy.

Dual division of focal plane polarimeters-based collinear reflection Mueller matrix fast imaging microscope.

SIGNIFICANCE: Reflection Mueller matrix imaging is suitable for characterizing the microstructure of bulk specimens and probing dynamic processes in living animals, there are always demands for speed and accuracy for such applications to avoid possible artifacts and reveal a sample's intrinsic properties. AIM: To demonstrate a design of collinear reflection Mueller matrix fast imaging microscope based on dual division of focal plane (DoFP) polarimeters (DoFPs-CRMMM) which has high measurement speed and accuracy. APPROACH: In DoFPs-CRMMM, to improve the measurement speed, we applied the dual DoFP polarimeters design on the collinear reflection system for the first time to achieve fast imaging in about 2 s. To improve the measurement accuracy, we improved the double-pass eigenvalue calibration method (dp-ECM) by background light correction, and explored the optimization of the set of reference samples. RESULTS: DoFPs-CRMMM was applied to measure the standard polarization samples and monitor the tissue optical clearing process of an artificial layered bulk tissue. Results show that the system has satisfactory performance which can capture the variation of polarization properties during the dynamic process. CONCLUSIONS: We present the establishment and demo application of DoFPs-CRMMM. The measurement speed can be further accelerated for potential applications in monitoring dynamic processes or living biomedical systems.

Deep learning for denoising in a Mueller matrix microscope.

The Mueller matrix microscope is a powerful tool for characterizing the microstructural features of a complex biological sample. Performance of a Mueller matrix microscope usually relies on two major specifications: measurement accuracy and acquisition time, which may conflict with each other but both contribute to the complexity and expenses of the apparatus. In this paper, we report a learning-based method to improve both specifications of a Mueller matrix microscope using a rotating polarizer and a rotating waveplate polarization state generator. Low noise data from long acquisition time are used as the ground truth. A modified U-Net structured network incorporating channel attention effectively reduces the noise in lower quality Mueller matrix images obtained with much shorter acquisition time. The experimental results show that using high quality Mueller matrix data as ground truth, such a learning-based method can achieve both high measurement accuracy and short acquisition time in polarization imaging.