Raman spectral unmixing via multimodal time-frequency transformations and deep learning.

Area of Science:

• Biomedical Optics
• Spectroscopy
• Medical Diagnostics

Background:

• Raman spectroscopy shows promise for disease diagnosis but struggles with complex biological tissues yielding mixed signals.
• In vivo Raman spectroscopy often collects signals from multiple chemical components and tissue types simultaneously.
• Accurate signal deconvolution is crucial for reliable clinical applications of Raman spectroscopy.

Purpose of the Study:

• To develop and validate a Raman spectral unmixing approach for separating mixed spectra from different biological tissues.
• To enhance the accuracy of in vivo Raman spectroscopy for disease diagnosis and research.
• To enable precise signal isolation from specific tissue components within complex biological samples.

Main Methods:

• Proposed a multimodal Raman spectral unmixing approach combining frequency and time-frequency transformations.
• Utilized an attention U-net model to predict target tissue spectra from each modality.
• Implemented multimodal fusion to filter and integrate information for accurate spectral unmixing.

Main Results:

• Successfully separated Raman spectra of subchondral bone and cartilage from mixed signals in canine knee joints.
• Demonstrated the capability of the method to isolate distinct tissue spectra from complex in vivo samples.
• Validated the accuracy of the unmixed spectra for potential application in osteoarthritis research.

Conclusions:

• The developed Raman spectral unmixing approach effectively distinguishes signals from different tissues in complex biological environments.
• This method significantly improves the potential for accurate in vivo disease detection and characterization using Raman spectroscopy.
• The technique offers a pathway for more precise biological detection, enabling separation of signals from diverse tissues and molecular components.