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Confocal Raman Microscopy with Adaptive Optics

Confocal Raman microscopy is a precise and label-free technique for analyzing thick samples at the microscale, but its use is often limited by weak Raman signals. Sample inhomogeneities introduce wavefront aberrations, further diminishing signal strength and requiring longer acquisition times. In this study, we present the first application of Adaptive Optics in confocal Raman microscopy to correct these aberrations, achieving substantial improvements in signal intensity and image quality. This approach integrates seamlessly with commercial microscopes without the need for hardware modifications. It utilizes a wavefront sensorless method, relying on an optofluidic, transmissive spatial light modulator attached to the microscope nosepiece to measure and correct aberrations. Experimental validation shows effective correction of aberrations in artificial scatterers and mouse brain tissue, enhancing spatial resolution and increasing signal intensity by up to 3.5 times. These results establ...
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SpMap

 

SpMap: Hyperspectral package for spectroscopists 

in Python 3 

Hyperspectral imaging offers new applications in agriculture, pharmaceutical, space, food, and upcoming applications such as medical diagnosis. SpMap stands out as a free package for the analysis of hyperspectral images. The package contains pre-processing, processing, and visualization. The preprocessing offers filtering and baseline correction, the processing offers to cluster and unmixing spectral components and visualization displays the processed data. SpMap defines a new hyperspectral object and further analysis considers this object. The Figure 1 shows the object structure.


SpMap also offers Raman calibration methods such as wavelength and intensity.

The figure below shows spectral components in a tumor sample of the bladder. The separation between protein and lipid molecules.
The next figure shows some results after processing and visualization of classification of tumor and nontumor bladder samples.



References

J. D. Munoz-Bolanos, SpectraMap, https://pypi.org/project/spectramap/.



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