SUMMARY
UCLA researchers in the Department of Electrical and Computer Engineering have developed a method for pathological crystal identification that is faster, has better contrast (regardless of orientation or size of crystals), and has better specificity compared to compensated polarized light microscopy. The method is fast, simple-to-operate, and compatible with all existing standard light microscopes without extensive or costly system modifications.
BACKGROUND
Pathological crystal identification is routinely practiced in diagnosing arthritic diseases. Compensated polarized light microscopy (CPLM), the current gold standard method for pathological crystal identification, uses polarized light to detect changes in the optical birefringence of a tissue and used to diagnose pathologies such as squamous cell carcinoma and gout. CPLM, however, offers only qualitative data for diagnosis and is subject to the experience of the user to detect low-contrast features, such as that of smaller crystals or crystals with weak birefringence. New tools that are less dependent on user experience and able to accurately detect low birefringent features are needed to improve arthritic patient diagnosis and analysis.
INNOVATION
UCLA researchers in the Department of Electrical and Computer Engineering have developed single-shot computational polarized light microscopy (SCPLM) that uses a polarization image sensor to identify pathological crystals in synovial fluid and other bodily fluids. By providing quantitative retardance and orientation map, SCPLM can generate pseudo-colored images from a single frame that resemble the color information of CPLM images with a major improvement in contrast.
The method has already been used to successfully used to identify 3 different types of crystals in synovial fluid (monosodium urate, calcium pyrophosphate dehydrate and triamcinolone acetonide crystals). The method reconstructed the birefringence information of these samples using a single image, without being affected by the orientation of individual crystals within the sample field of view. SCPLM was faster, had better contrast (regardless of orientation or size of crystals), and specificity compared to CPLM.
POTENTIAL APPLICATIONS
- Diagnostic tool
- Microscopy
- Rheumatology
- Crystal formation analysis
- Development of machine learning-based automated processing and diagnostic analysis of pathological crystal samples
ADVANTAGES
- Fast
- High Sensitivity
- Quantitative retardance and orientation map data
- Simple-to-operate
- Microscope compatibility
- Low Cost
RELATED MATERIALS
STATUS OF DEVELOPMENT
Successful demonstration of method on different types of crystals in biological samples was performed.