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.

Mueller matrix microscope: a quantitative tool to facilitate detections and fibrosis scorings of liver cirrhosis and cancer tissues.

Today the increasing cancer incidence rate is becoming one of the biggest threats to human health.Among all types of cancers, liver cancer ranks in the top five in both frequency and mortality rate all over the world. During the development of liver cancer, fibrosis often evolves as part of a healing process in response to liver damage, resulting in cirrhosis of liver tissues. In a previous study, we applied the Mueller matrix microscope to pathological liver tissue samples and found that both the Mueller matrix polar decomposition (MMPD) and Mueller matrix transformation (MMT) parameters are closely related to the fibrous microstructures. In this paper,we take this one step further to quantitatively facilitate the fibrosis detections and scorings of pathological liver tissue samples in different stages from cirrhosis to cancer using the Mueller matrix microscope. The experimental results of MMPD and MMT parameters for the fibrotic liver tissue samples in different stages are measured and analyzed. We also conduct Monte Carlo simulations based on the sphere birefringence model to examine in detail the influence of structural changes in different fibrosis stages on the imaging parameters. Both the experimental and simulated results indicate that the polarized light microscope and transformed Mueller matrix parameter scan provide additional quantitative information helpful for fibrosis detections and scorings of liver cirrhosis and cancers. Therefore, the polarized light microscope and transformed Mueller matrix parameters have a good application prospect in liver cancer diagnosis.

Differentiating characteristic microstructural features of cancerous tissues using Mueller matrix microscope.

Polarized light imaging can provide rich microstructural information of samples, and has been applied to the detections of various abnormal tissues. In this paper, we report a polarized light microscope based on Mueller matrix imaging by adding the polarization state generator and analyzer (PSG and PSA) to a commercial transmission optical microscope. The maximum errors for the absolute values of Mueller matrix elements are reduced to 0.01 after calibration. This Mueller matrix microscope has been used to examine human cervical and liver cancerous tissues with fibrosis. Images of the transformed Mueller matrix parameters provide quantitative assessment on the characteristic features of the pathological tissues. Contrast mechanism of the experimental results are backed up by Monte Carlo simulations based on the sphere-cylinder birefringence model, which reveal the relationship between the pathological features in the cancerous tissues at the cellular level and the polarization parameters. Both the experimental and simulated data indicate that the microscopic transformed Mueller matrix parameters can distinguish the breaking down of birefringent normal tissues for cervical cancer, or the formation of birefringent surrounding structures accompanying the inflammatory reaction for liver cancer. With its simple structure, fast measurement and high precision, polarized light microscope based on Mueller matrix shows a good diagnosis application prospect.