Discriminative Addressing of Versatile Nanodiamonds via Physically-Enabled Classifier in Complex Bio-Systems

TL;DR

This paper introduces a novel all-optical imaging method using a physically-enabled classifier to enhance detection of nanodiamonds in complex biological environments, significantly reducing background noise and improving imaging contrast.

Contribution

It presents a new optical modulation and classification technique for nanodiamond fluorescence detection that effectively filters background noise in biological tissues.

Findings

Achieved nearly 10^6 times enhancement of signal-to-background ratio in neural imaging.

Demonstrated 4-fold contrast improvement in magnetic resonance measurements inside cells.

Validated the method across various complex biological scenarios with fluorescence interference.

Abstract

Nitrogen-vacancy (NV) centers show great potentials for nanoscale bio-sensing and bio-imaging. Nevertheless, their envisioned bio-applications suffer from intrinsic background noise due to unavoidable light scattering and autofluorescence in cells and tissues. Herein, we develop a novel all-optical modulated imaging method via physically-enabled classifier, for on-demand and direct access to NV fluorescence at pixel resolution while effectively filtering out background noise. Specifically, NV fluorescence can be modulated optically to exhibit sinusoid-like variations, providing basis for classification. We validate our method in various complex biological scenarios with fluorescence interference, ranging from cells to organisms. Notably, our classification-based approach achieves almost 10^6 times enhancement of signal-to-background ratio (SBR) for fluorescent nanodiamonds (FNDs) in neural protein imaging. We also demonstrate 4-fold contrast improvement in optically-detected magnetic resonance measurements (ODMR) of FNDs inside stained cells. Our technique offers a generic, explainable and robust solution, applicable for realistic high-fidelity imaging and sensing in challenging noise-laden scenarios.