Luminescence-enabled three-dimensional temperature bioimaging

Area of Science:

• Nanotechnology
• Biophysics
• Machine Learning

Background:

• Luminescence thermometry offers remote, high-resolution thermal imaging.
• Current methods are limited to 2D thermal imaging, hindering 3D heat diffusion analysis.
• A robust 3D luminescence thermometry technique is needed for advanced biological studies.

Purpose of the Study:

• To develop a credible method for extracting 3D thermal images using luminescence.
• To combine Ag2S nanothermometers with machine learning for 3D thermal sensing.
• To overcome the limitations of existing 2D luminescence thermometry.

Main Methods:

• Utilized silver sulfide (Ag2S) nanothermometers for temperature sensing.
• Employed machine learning algorithms, specifically optimized neural networks.
• Leveraged spectral distortions caused by temperature and tissue photon extinction.

Main Results:

• Successfully extracted 3D thermal images from complex nanothermometer patterns.
• Demonstrated the method's efficacy for in vivo luminescence thermometry.
• The algorithm accurately reconstructed 3D temperature distributions.

Conclusions:

• The developed method provides the first credible approach for 3D luminescence thermometry.
• This technique significantly advances 3D thermal imaging capabilities in biological systems.
• Has broad implications for 3D sensing applications in biology and beyond.