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

ABSTRACT

High‐resolution biological imaging in deep tissues holds substantial importance for advancing precision medicine. Currently, the lanthanide‐doped nanoparticles enable in vivo near‐infrared imaging but face a critical trade‐off: Er ^{3 +} ‐based emission at around 1530 nm enables high optical resolution yet suffers from limited tissue penetration due to water absorption, whereas nanoprobes emitting at approximately 980/1060 nm offer deeper penetration with high brightness but compromised resolution due to higher tissue scattering at shorter wavelength. This fundamental contradiction between imaging depth and resolution remains a key challenge. Herein, we introduce the concept of energy traps to actively regulate energy distribution within lanthanide nanoparticles via excitation‐wavelength switching and directional energy transfer modulation. This strategy enables controlled access to either sensitizers (Yb ³⁺ , Nd ³⁺ ) self‐emission or efficient sensitization of activators (Er ³⁺ ), thereby allowing selective enhancement of emission channels. By integrating the deep‐tissue penetration capability of short‐wavelength sensitizers (980 and 1060 nm) with the high‐resolution emission of long‐wavelength activators (1530 nm) through a deep‐learning‐based network, we successfully achieved a 93% enhancement in imaging performance for short‐wavelength probes, offering a robust and adaptable platform for high‐contrast deep‐tissue bioimaging and future point‐of‐care diagnostics.