Quantum-enhanced nonlinear imaging

Introduction

Many pioneering advances in medicine and biology require observation of the microscopic world with high resolution and without damaging the specimen. One of the most widespread techniques is multiphoton fluorescence microscopy, which allows full 3D imaging via optical sectioning, i.e., imaging of planes within the sample without the need for physical slicing.

Limitations of Current Techniques

This technique has a major limitation, however: the penetration depth and the signal-to-noise ratio are not sufficient for imaging deep within tissue, preventing functional imaging of, e.g., neuronal or cardiac activity beyond superficial layers.

Project Overview

QuNIm aims to transform the field of nonlinear imaging and microscopy by exploiting the unique properties of entanglement, a quantum mechanical superposition of two or more photons that behave like single particles.

Key Innovations

1. Two quantum-correlated photons are absorbed in a nonlinear process as a single particle, an event 10 billion times more probable than the absorption of two classical photons.
2. QuNIm will apply, for the first time, the innovative concepts of:
   • Spatiotemporal and multimode entanglement
   • Super-Poissonian fluctuations
   • Macroscopic quantum beams

These innovations will deliver a ground-breaking imaging technique.

Advantages of QuNIm

QuNIm will maintain the strengths of standard nonlinear imaging (e.g., multiphoton microscopy, boasting high resolution, 3D imaging, and molecular specificity using fluorophores/photoproteins) while increasing its penetration depth and removing the drawbacks such as:

• Complex ultrashort pulsed lasers
• Lengthy scanning procedures
• Phototoxicity

Impact on Deep-Tissue Imaging

QuNIm will further extend the limit of deep-tissue imaging while at the same time enhancing the contrast and reducing the laser intensity, thereby mitigating tissue damage. This will deliver a transformative impact in different fields.

Applications in Neuroscience

For example, in neuroscience, this will allow imaging of sub-cortical brain regions fundamental for important studies into learning, memory, and degenerative neural conditions such as Alzheimer's disease.