Deep-Learning-Enhanced Bioimaging Via Energy Traps Regulated Lanthanide Nanoparticles

Area of Science:

• Biomedical Engineering
• Materials Science
• Nanotechnology

Background:

• High-resolution biological imaging in deep tissues is crucial for precision medicine.
• Lanthanide-doped nanoparticles offer in vivo near-infrared imaging but face a resolution-depth trade-off.
• Er3+-based emission (1530 nm) provides high resolution but limited penetration; 980/1060 nm emission offers deeper penetration but lower resolution.

Purpose of the Study:

• To overcome the fundamental contradiction between imaging depth and resolution in deep-tissue bioimaging.
• To introduce a strategy for actively regulating energy distribution within lanthanide nanoparticles.
• To enable selective enhancement of emission channels for improved imaging performance.

Main Methods:

• Introduced energy traps to regulate energy distribution in lanthanide nanoparticles.
• Utilized excitation-wavelength switching and directional energy transfer modulation.
• Integrated deep-tissue penetration with high-resolution emission using a deep-learning network.

Main Results:

• Achieved controlled access to sensitizer self-emission or activator sensitization.
• Enabled selective enhancement of specific emission channels.
• Demonstrated a 93% enhancement in imaging performance for short-wavelength probes.

Conclusions:

• Developed a robust and adaptable platform for high-contrast deep-tissue bioimaging.
• The energy trap strategy effectively combines deep penetration and high resolution.
• Paved the way for advanced point-of-care diagnostics and precision medicine.